Collaborators: Shane Blowes, Jon Chase, Helmut Hillebrand, Michael Burrows, Amanda Bates, Uli Brose, Benoit Gauzens, Laura Antao, Ruben Remelgado, Carsten Meyer, Myriam Hirt, maybe others Assistance: Katherine Lew, Josef Hauser

Introduction

Methods

library(data.table) # for handling large datasets
library(ggplot2) # for some plotting
library(nlme) # for ME models
library(maps) # for map
library(gridExtra) # to combine ggplots together
library(grid) # to combine ggplots together
library(RColorBrewer)
library(MASS) # for stepAIC

options(width=500) # turn off most text wrapping

# tell RStudio to use project root directory as the root for this notebook. Needed since we are storing code in a separate directory.
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file()) 
# Turnover and covariates assembled by turnover_vs_temperature_prep.Rmd
trends <- fread('output/turnover_w_covariates.csv.gz')

# set realm order
trends[, REALM := factor(REALM, levels = c('Freshwater', 'Marine', 'Terrestrial'), ordered = FALSE)]

# set up sign of temperature change
trends[, tsign := factor(sign(temptrend))]

# realm that combined Terrestrial and Freshwater, for interacting with human impact
trends[, REALM2 := REALM]
levels(trends$REALM2) = list(TerrFresh = "Freshwater", TerrFresh = "Terrestrial", Marine = "Marine")

# group Marine invertebrates/plants in with All
trends[, taxa_mod2 := taxa_mod]
trends[taxa_mod == 'Marine invertebrates/plants', taxa_mod2 := 'All']

# calculate duration
trends[, duration := maxyrBT - minyrBT + 1]

# trim to data with >= 3 yrs
trends <- trends[nyrBT >= 3, ]

Log-transform some variables, then center and scale.

trends[, tempave.sc := scale(tempave)]
trends[, tempave_metab.sc := scale(tempave_metab)]
trends[, seas.sc := scale(seas)]
trends[, microclim.sc := scale(log(microclim))]
trends[, temptrend.sc := scale(temptrend, center = FALSE)] # do not center
trends[, temptrend_abs.sc := scale(abs(temptrend), center = FALSE)] # do not center, so that 0 is still 0 temperature change
trends[, mass.sc := scale(log(mass_mean_weight))]
trends[, speed.sc := scale(log(speed_mean_weight+1))]
trends[, lifespan.sc := scale(log(lifespan_mean_weight))]
trends[, consumerfrac.sc := scale(consfrac)]
trends[, endothermfrac.sc := scale(endofrac)]
trends[, nspp.sc := scale(log(Nspp))]
trends[, thermal_bias.sc := scale(thermal_bias)]
trends[, npp.sc := scale(log(npp))]
trends[, veg.sc := scale(log(veg+1))]
trends[, duration.sc := scale(log(duration))]
trends[, human_bowler.sc := scale(log(human_bowler+1)), by = REALM2] # separate scaling by realm
trends[REALM2 == 'TerrFresh', human_footprint.sc := scale(log(human_venter+1))]
trends[REALM2 == 'Marine', human_footprint.sc := scale(log(human_halpern))]

Examine how many data points are available

Just turnover

cat('Overall # time-series: ', nrow(trends), '\n')
Overall # time-series:  39195 
cat('# studies: ', trends[, length(unique(STUDY_ID))], '\n')
# studies:  307 
cat('Data points: ', trends[, sum(nyrBT)], '\n')
Data points:  266337 
trends[, table(REALM)]
REALM
 Freshwater      Marine Terrestrial 
        628       35742        2825 
trends[, table(taxa_mod)]
taxa_mod
                        All                  Amphibians                     Benthos                       Birds 
                       1447                         352                        4310                        8719 
                       Fish               Invertebrates                     Mammals Marine invertebrates/plants 
                      21708                        1799                         504                         104 
                      Plant                    Reptiles 
                        248                           4 
trends[, table(taxa_mod, REALM)]
                             REALM
taxa_mod                      Freshwater Marine Terrestrial
  All                                  0   1444           3
  Amphibians                           2      0         350
  Benthos                              0   4310           0
  Birds                                0   6542        2177
  Fish                               610  21098           0
  Invertebrates                       14   1705          80
  Mammals                              0    459          45
  Marine invertebrates/plants          0    104           0
  Plant                                1     80         167
  Reptiles                             1      0           3

With all covariates (Bowler for human)

# the cases we can compare
apply(trends[, .(Jtutrendrem0, REALM, tempave.sc, tempave_metab.sc, seas.sc, microclim.sc, temptrend.sc, mass.sc, speed.sc, lifespan.sc, consumerfrac.sc, endothermfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)], MARGIN = 2, FUN = function(x) sum(!is.na(x)))
    Jtutrendrem0            REALM       tempave.sc tempave_metab.sc          seas.sc     microclim.sc 
           39195            39195            36747            36747            36747            38291 
    temptrend.sc          mass.sc         speed.sc      lifespan.sc  consumerfrac.sc endothermfrac.sc 
           36747            39114            39082            38045            39195            39195 
         nspp.sc  thermal_bias.sc           npp.sc           veg.sc  human_bowler.sc 
           39195            36286            39089            39099            39195 
i <- trends[, complete.cases(Jtutrendrem0, tempave.sc, tempave_metab.sc, seas.sc, microclim.sc, temptrend.sc, mass.sc, speed.sc, lifespan.sc, consumerfrac.sc, endothermfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)]
cat('Overall # time-series: ', sum(i), '\n')
Overall # time-series:  36017 
cat('# studies: ', trends[i, length(unique(STUDY_ID))], '\n')
# studies:  231 
cat('Data points: ', trends[i, sum(nyrBT)], '\n')
Data points:  243237 
trends[i, table(REALM)]
REALM
 Freshwater      Marine Terrestrial 
        608       33098        2311 
trends[i, table(taxa_mod)]
taxa_mod
                        All                  Amphibians                     Benthos                       Birds 
                       1422                          12                        4288                        7198 
                       Fish               Invertebrates                     Mammals Marine invertebrates/plants 
                      20810                        1517                         495                         104 
                      Plant                    Reptiles 
                        169                           2 
trends[i, table(taxa_mod, REALM)]
                             REALM
taxa_mod                      Freshwater Marine Terrestrial
  All                                  0   1420           2
  Amphibians                           2      0          10
  Benthos                              0   4288           0
  Birds                                0   5116        2082
  Fish                               597  20213           0
  Invertebrates                        8   1443          66
  Mammals                              0    459          36
  Marine invertebrates/plants          0    104           0
  Plant                                1     55         113
  Reptiles                             0      0           2

Choose the variance structure for mixed effects models

Try combinations of

  • variance scaled to a power of the number of years in the community time-series
  • variance scaled to a power of the abs temperature trend
  • random intercept for taxa_mod
  • random intercept for STUDY_ID
  • random slope (abs temperature trend) for taxa_mod
  • random slope (abs temperature trend) for STUDY_ID
  • random intercept for rarefyID (for overdispersion)

And choose the one with lowest AIC (not run: takes a long time)

# fit models for variance structure
fixed <- formula(Jtutrendrem0 ~ temptrend_abs.sc*REALM +
                     temptrend_abs.sc*tsign + 
                     temptrend_abs.sc*tempave_metab.sc + 
                     temptrend_abs.sc*seas.sc + 
                     temptrend_abs.sc*microclim.sc + 
                     temptrend_abs.sc*mass.sc + 
                     temptrend_abs.sc*speed.sc + 
                     temptrend_abs.sc*consumerfrac.sc +
                     temptrend_abs.sc*nspp.sc +
                     temptrend_abs.sc*thermal_bias.sc:tsign +
                     temptrend_abs.sc*npp.sc +
                     temptrend_abs.sc*veg.sc +
                     temptrend_abs.sc*duration.sc +
                     temptrend_abs.sc*human_bowler.sc:REALM2)
i <- trends[, complete.cases(Jtutrendrem0, temptrend_abs.sc, REALM, tsign, tempave_metab.sc, seas.sc, 
                             microclim.sc, mass.sc, speed.sc, consumerfrac.sc, nspp.sc,
                             thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)]
mods <- vector('list', 0)
mods[[1]] <- gls(fixed, data = trends[i,])
mods[[2]] <- gls(fixed, data = trends[i,], weights = varPower(-0.5, ~nyrBT))
mods[[3]] <- gls(fixed, data = trends[i,], weights = varPower(0.5, ~temptrend_abs.sc))

mods[[4]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2, control = lmeControl(opt = "optim"))
mods[[5]] <- lme(fixed, data = trends[i,], random = ~1|STUDY_ID, control = lmeControl(opt = "optim"))
mods[[6]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2/STUDY_ID, control = lmeControl(opt = "optim"))
mods[[7]] <- lme(fixed, data = trends[i,], random = ~1|STUDY_ID/rarefyID, control = lmeControl(opt = "optim"))
mods[[8]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2/STUDY_ID/rarefyID, control = lmeControl(opt = "optim"))

mods[[9]] <- lme(fixed, data = trends[i,], random = ~temptrend_abs.sc | taxa_mod)
mods[[10]] <- lme(fixed, data = trends[i,], random = list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)) # includes overdispersion. new formula so that random slope is only for study level (not enough data to extend to rarefyID).

mods[[11]] <- lme(fixed, data = trends[i,], random = list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1), weights = varPower(-0.5, ~nyrBT))
mods[[12]] <- lme(fixed, data = trends[i,], random = list(taxa_mod2 = ~ temptrend_abs.sc, STUDY_ID = ~ 1, rarefyID = ~1), weights = varPower(-0.5, ~nyrBT))

aics <- sapply(mods, AIC)
minaics <- aics - min(aics)
minaics
which.min(aics)

Chooses the random slopes (temptrend_abs) & intercepts for STUDY_ID, overdispersion, and variance scaled to number of years. We haven’t dealt with potential testing on the boundary issues here yet.

Results

Where do we have data?

world <- map_data('world')
ggplot(world, aes(x = long, y = lat, group = group)) +
    geom_polygon(fill = 'lightgray', color = 'white') +
    geom_point(data = trends, aes(rarefyID_x, rarefyID_y, group = REALM, color = REALM), size = 0.5, alpha = 0.4)  +
    scale_color_brewer(palette="Set1", name = 'Realm') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=16),
        axis.title=element_text(size=20)) +
  labs(x = 'Longitude (°)', y = 'Latitude (°)')

Mostly northern hemisphere, but spread all over. Not so much in Africa or much of Asia.

Average rates of turnover (without year 1)

trends[abs(temptrend) >= 0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                                sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # turnover per year for locations changing temperature
trends[abs(temptrend) < 0.1, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                               sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # not changing temperature
trends[temptrend >= 0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                           sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # warming
trends[temptrend <= -0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                            sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # cooling

trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) < 35, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # tropics and sub-tropics
trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) >= 35 & abs(rarefyID_y) < 66.56339, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # temperate
trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) >= 66.56339, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # arctic

Temperature-only model (Jtutrend, Jbetatrend, Horntrend)

i4 <- trends[, complete.cases(Jtutrendrem0, REALM, temptrend)]

randef <- list(STUDY_ID = ~ abs(temptrend), rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

if(file.exists('temp/modonlyTtrendrem0.rds')){
  modonlyTtrendrem0 <- readRDS('temp/modonlyTtrendrem0.rds')
} else {
  modonlyTtrendrem0 <- lme(Jtutrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i4,], method = 'REML')
  saveRDS(modonlyTtrendrem0, file = 'temp/modonlyTtrendrem0.rds')
}

i5 <- trends[, complete.cases(Jbetatrendrem0, REALM, temptrend)]
if(file.exists('temp/modonlyTtrendJbetarem0.rds')){
  modonlyTtrendJbetarem0 <- readRDS('temp/modonlyTtrendJbetarem0.rds')
} else {
  modonlyTtrendJbetarem0 <- lme(Jbetatrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i5,], method = 'REML', 
                   control=lmeControl(msMaxIter = 100, maxIter = 100))
  saveRDS(modonlyTtrendJbetarem0, file = 'temp/modonlyTtrendJbetarem0.rds')
}

i6 <- trends[, complete.cases(Horntrendrem0, REALM, temptrend)]
if(file.exists('temp/modonlyTtrendHornrem0.rds')){
  modonlyTtrendHornrem0 <- readRDS('temp/modonlyTtrendHornrem0.rds')
} else {
  modonlyTtrendHornrem0 <- lme(Horntrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i6,], method = 'REML')
  saveRDS(modonlyTtrendHornrem0, file = 'temp/modonlyTtrendHornrem0.rds')
}

summary(modonlyTtrendrem0)
Linear mixed-effects model fit by REML
 Data: trends[i4, ] 

Random effects:
 Formula: ~abs(temptrend) | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
               StdDev      Corr  
(Intercept)    0.007657053 (Intr)
abs(temptrend) 0.197650298 -0.935

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.01097849 2.000161

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.121022 
Fixed effects: Jtutrendrem0 ~ abs(temptrend) * REALM 
 Correlation: 
                                (Intr) abs(t) REALMM REALMT a():REALMM
abs(temptrend)                  -0.807                                
REALMMarine                     -0.947  0.764                         
REALMTerrestrial                -0.901  0.727  0.853                  
abs(temptrend):REALMMarine       0.766 -0.949 -0.814 -0.690           
abs(temptrend):REALMTerrestrial  0.733 -0.908 -0.693 -0.810  0.862    

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-6.57101792 -0.22892007 -0.01904447  0.27434430  5.64177564 

Number of Observations: 36747
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   292                  36747 
summary(modonlyTtrendJbetarem0)
Linear mixed-effects model fit by REML
 Data: trends[i5, ] 

Random effects:
 Formula: ~abs(temptrend) | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
               StdDev      Corr  
(Intercept)    0.005965236 (Intr)
abs(temptrend) 0.160013131 -0.038

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept)  Residual
StdDev: 0.003861919 0.9688156

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-1.887978 
Fixed effects: Jbetatrendrem0 ~ abs(temptrend) * REALM 
 Correlation: 
                                (Intr) abs(t) REALMM REALMT a():REALMM
abs(temptrend)                  -0.543                                
REALMMarine                     -0.941  0.511                         
REALMTerrestrial                -0.906  0.492  0.852                  
abs(temptrend):REALMMarine       0.515 -0.948 -0.524 -0.467           
abs(temptrend):REALMTerrestrial  0.494 -0.908 -0.464 -0.542  0.861    

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-8.57430409 -0.30896466 -0.02462766  0.32539894  8.23224466 

Number of Observations: 36747
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   292                  36747 
summary(modonlyTtrendHornrem0)
Linear mixed-effects model fit by REML
 Data: trends[i6, ] 

Random effects:
 Formula: ~abs(temptrend) | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
               StdDev     Corr  
(Intercept)    0.01201968 (Intr)
abs(temptrend) 0.25314398 0.016 

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.01882813 2.441788

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.318769 
Fixed effects: Horntrendrem0 ~ abs(temptrend) * REALM 
 Correlation: 
                                (Intr) abs(t) REALMM REALMT a():REALMM
abs(temptrend)                  -0.472                                
REALMMarine                     -0.944  0.446                         
REALMTerrestrial                -0.899  0.424  0.848                  
abs(temptrend):REALMMarine       0.447 -0.945 -0.455 -0.401           
abs(temptrend):REALMTerrestrial  0.428 -0.906 -0.404 -0.473  0.856    

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-5.75457482 -0.22483445 -0.02254226  0.23615730  5.73488343 

Number of Observations: 36005
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   257                  36005 

Plot the temp-only coefficients

colors <- brewer.pal(3, 'Dark2')

# make table of coefficients
coefs1 <- as.data.frame(summary(modonlyTtrendrem0)$tTable)
coefs2 <- as.data.frame(summary(modonlyTtrendJbetarem0)$tTable)
coefs3 <- as.data.frame(summary(modonlyTtrendHornrem0)$tTable)
coefs1$mod <- 'Jtu'
coefs2$mod <- 'Jbeta'
coefs3$mod <- 'Horn'
rows1 <- which(grepl('temptrend', rownames(coefs1))) # extract temperature effect
cols <- c('Value', 'Std.Error', 'mod')
allcoefs <- rbind(coefs1[rows1, cols], coefs2[rows1, cols], coefs3[rows1, cols])
allcoefs$Value[grepl('REALMMarine', rownames(allcoefs))] <- 
  allcoefs$Value[grepl('REALMMarine', rownames(allcoefs))] + 
  allcoefs$Value[!grepl('REALM', rownames(allcoefs))] # add intercept to marine effects
allcoefs$Value[grepl('REALMTerrestrial', rownames(allcoefs))] <- 
  allcoefs$Value[grepl('REALMTerrestrial', rownames(allcoefs))] + 
  allcoefs$Value[!grepl('REALM', rownames(allcoefs))] # add intercept to terrestrial effects

allcoefs$lCI <- allcoefs$Value - allcoefs$Std.Error # lower confidence interval
allcoefs$uCI <- allcoefs$Value + allcoefs$Std.Error
allcoefs$y <- c(3, 2, 1) + rep(c(0, -0.1, -0.2), c(3, 3, 3)) # y-values
allcoefs$col <- c(rep(colors[1], 3), rep(colors[2], 3), rep(colors[3], 3))
allcoefs$realm <- rep(c('Freshwater', 'Marine', 'Terrestrial'), 3)

par(las = 1, mai = c(0.8, 2, 0.1, 0.1))
plot(0,0, col = 'white', xlim=c(-0.1, 0.85), ylim = c(0.5,3), 
     yaxt='n', xlab = 'Turnover per |°C/yr|', ylab ='')
axis(2, at = 3:1, labels = c('Freshwater', 'Marine', 'Terrestrial'), cex.axis = 0.7)
abline(v = 0, col = 'grey')
for(i in 1:nrow(allcoefs)){
  with(allcoefs[i, ], points(Value, y, pch = 16, col = col))
  with(allcoefs[i, ], lines(x = c(lCI, uCI), y = c(y, y), col = col))
}
legend('bottomright', col = colors, lwd = 1, pch = 16, 
       legend = c('Jaccard turnover', 'Jaccard total', 'Horn-Morisita',
                  'Jaccard turnover rem0', 'Jaccard total rem0', 'Horn-Morisita rem0'))

Nicer plots of turnover vs. temperature data

Violin plots

# on macbook: fig.width=3, fig.height=2.375, fig.retina=3, out.width=3, out.height=2.375
# on external monitor: fig.width=6, fig.height=4.5
trends[temptrend <= -0.7, temptrendtext := 'Cooling']
trends[abs(temptrend) <= 0.05, temptrendtext := 'Stable']
trends[temptrend >= 0.7, temptrendtext := 'Warming']

trends[abs(rarefyID_y) < 35, latzone := 'Subtropics']
trends[abs(rarefyID_y) >= 35 & abs(rarefyID_x) < 66.56339, latzone := 'Temperate'] 
trends[abs(rarefyID_y) >= 66.56339, latzone := 'Polar']

p1 <- ggplot(trends[!is.na(temptrendtext), ], aes(temptrendtext, Horntrendrem0)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), fill = 'grey') +
  labs(x = '', y = 'Turnover', tag = 'A', title = 'Rate of temperature change') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=8),
        axis.title=element_text(size=10))

p2 <- ggplot(trends[abs(temptrend) >= 0.1 & !is.na(latzone), ], aes(latzone, Horntrendrem0)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), fill = 'grey') + 
  labs(x = '', y = '', tag = 'C', title = 'Warming regions') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=7),
        axis.title=element_text(size=10))


grid.arrange(p1, p2, ncol = 2)

Full models

Try static covariates plus interactions of abs temperature trend with each covariate:

  • realm
  • speed
  • mass
  • average metabolic temperature
  • consumer fraction
  • environmental temperature
  • seasonality
  • microclimates
  • thermal bias
  • NPP
  • vegetation
  • duration
  • human footprint

Except for thermal bias: interact with temperature trend (not abs)

Fit full models

Bowler vs Venter/Halpern human impact

Bowler has lower AIC.

Full models

i1 <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i2 <- trends[, complete.cases(Jbetatrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i3 <- trends[, complete.cases(Horntrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

# full models
if(file.exists('temp/modTfullJturem0.rds')){
  modTfullJturem0 <- readRDS('temp/modTfullJturem0.rds')
} else {
  modTfullJturem0 <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'REML')
  saveRDS(modTfullJturem0, file = 'temp/modTfullJturem0.rds')
}

if(file.exists('temp/modTfullJbetarem0.rds')){
  modTfullJbetarem0 <- readRDS('temp/modTfullJbetarem0.rds')
} else {
  modTfullJbetarem0 <- lme(Jbetatrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'REML')
  saveRDS(modTfullJbetarem0, file = 'temp/modTfullJbetarem0.rds')
}

if(file.exists('temp/modTfullHornrem0.rds')){
  modTfullHornrem0 <- readRDS('temp/modTfullHornrem0.rds')
} else {
  modTfullHornrem0 <- lme(Horntrendrem0 ~ temptrend_abs.sc*REALM + 
                        temptrend_abs.sc*tsign +
                        temptrend_abs.sc*tempave_metab.sc + 
                        temptrend_abs.sc*seas.sc + 
                        temptrend_abs.sc*microclim.sc + 
                        temptrend_abs.sc*mass.sc + 
                        temptrend_abs.sc*speed.sc + 
                        temptrend_abs.sc*consumerfrac.sc +
                        temptrend_abs.sc*nspp.sc +
                        temptrend_abs.sc*thermal_bias.sc:tsign +
                        temptrend_abs.sc*npp.sc +
                        temptrend_abs.sc*veg.sc +
                        temptrend_abs.sc*duration.sc +
                        temptrend_abs.sc*human_bowler.sc:REALM2,
                      random = randef, weights = varef, data = trends[i3,], method = 'REML')
  saveRDS(modTfullHornrem0, file = 'temp/modTfullHornrem0.rds')
}

summary(modTfullJturem0)
Linear mixed-effects model fit by REML
 Data: trends[i2, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev      Corr  
(Intercept)      0.009402743 (Intr)
temptrend_abs.sc 0.024575808 -0.966

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.01091239 2.006389

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.129145 
Fixed effects: Jtutrendrem0 ~ temptrend_abs.sc * REALM + temptrend_abs.sc *      tsign + temptrend_abs.sc * tempave_metab.sc + temptrend_abs.sc *      seas.sc + temptrend_abs.sc * microclim.sc + temptrend_abs.sc *      mass.sc + temptrend_abs.sc * speed.sc + temptrend_abs.sc *      consumerfrac.sc + temptrend_abs.sc * nspp.sc + temptrend_abs.sc *      thermal_bias.sc:tsign + temptrend_abs.sc * npp.sc + temptrend_abs.sc *      veg.sc + temptrend_abs.sc * duration.sc + temptrend_abs.sc *      human_bowler.sc:REALM2 
 Correlation: 
                                                 (Intr) tmpt_. REALMM REALMT tsign1 tmpv_. ses.sc mcrcl. mss.sc
temptrend_abs.sc                                 -0.797                                                        
REALMMarine                                      -0.955  0.757                                                 
REALMTerrestrial                                 -0.683  0.574  0.634                                          
tsign1                                           -0.095  0.063  0.007  0.012                                   
tempave_metab.sc                                  0.061 -0.037 -0.053 -0.242  0.074                            
seas.sc                                          -0.150  0.143  0.202 -0.097 -0.070  0.138                     
microclim.sc                                     -0.068  0.047  0.077  0.006  0.015 -0.157  0.097              
mass.sc                                           0.061 -0.042 -0.036 -0.038 -0.019  0.100  0.127 -0.001       
speed.sc                                          0.107 -0.093 -0.057 -0.084 -0.073 -0.194 -0.055  0.070 -0.437
consumerfrac.sc                                  -0.028  0.022  0.017  0.133  0.014 -0.135 -0.055  0.017  0.016
nspp.sc                                           0.020  0.000 -0.074 -0.066  0.049 -0.154 -0.037 -0.113 -0.010
npp.sc                                            0.041 -0.044 -0.061  0.054  0.041  0.037 -0.136 -0.187 -0.027
veg.sc                                           -0.578  0.387  0.599  0.024 -0.006  0.007  0.080 -0.010  0.021
duration.sc                                      -0.037  0.008  0.032  0.016 -0.160  0.085 -0.006 -0.016 -0.057
temptrend_abs.sc:REALMMarine                      0.764 -0.951 -0.804 -0.534 -0.014  0.030 -0.184 -0.056  0.020
temptrend_abs.sc:REALMTerrestrial                 0.542 -0.693 -0.499 -0.820 -0.018  0.182  0.078  0.024  0.033
temptrend_abs.sc:tsign1                           0.067 -0.140 -0.022 -0.014 -0.507 -0.056  0.012 -0.006 -0.005
temptrend_abs.sc:tempave_metab.sc                -0.037  0.042  0.028  0.171 -0.058 -0.788 -0.106  0.091 -0.066
temptrend_abs.sc:seas.sc                          0.145 -0.158 -0.183  0.095  0.053 -0.100 -0.777 -0.099 -0.081
temptrend_abs.sc:microclim.sc                     0.038 -0.062 -0.047  0.020 -0.014  0.088 -0.068 -0.760  0.028
temptrend_abs.sc:mass.sc                         -0.040  0.048  0.010  0.020 -0.002 -0.086 -0.066  0.019 -0.701
temptrend_abs.sc:speed.sc                        -0.084  0.104  0.040  0.073  0.069  0.167  0.031 -0.041  0.266
temptrend_abs.sc:consumerfrac.sc                  0.030 -0.037 -0.018 -0.117 -0.007  0.105  0.038  0.009  0.006
temptrend_abs.sc:nspp.sc                          0.009  0.008  0.038  0.037 -0.033  0.143  0.035  0.069  0.033
tsign-1:thermal_bias.sc                           0.061 -0.041 -0.057 -0.046 -0.078  0.132 -0.217 -0.015 -0.043
tsign1:thermal_bias.sc                            0.095 -0.074 -0.103 -0.097  0.063  0.223 -0.330 -0.100 -0.043
temptrend_abs.sc:npp.sc                          -0.039  0.065  0.059 -0.051 -0.028 -0.007  0.116  0.168 -0.004
temptrend_abs.sc:veg.sc                           0.411 -0.507 -0.431  0.022 -0.008 -0.018 -0.115  0.003 -0.018
                                                 spd.sc cnsmr. nspp.s npp.sc veg.sc drtn.s t_.:REALMM t_.:REALMT
temptrend_abs.sc                                                                                                
REALMMarine                                                                                                     
REALMTerrestrial                                                                                                
tsign1                                                                                                          
tempave_metab.sc                                                                                                
seas.sc                                                                                                         
microclim.sc                                                                                                    
mass.sc                                                                                                         
speed.sc                                                                                                        
consumerfrac.sc                                  -0.144                                                         
nspp.sc                                           0.184  0.094                                                  
npp.sc                                            0.125 -0.045 -0.188                                           
veg.sc                                           -0.019 -0.010  0.024 -0.178                                    
duration.sc                                      -0.009  0.005 -0.252  0.062 -0.012                             
temptrend_abs.sc:REALMMarine                      0.047 -0.015  0.046  0.064 -0.406 -0.018                      
temptrend_abs.sc:REALMTerrestrial                 0.068 -0.104  0.025 -0.047  0.019 -0.005  0.639               
temptrend_abs.sc:tsign1                           0.041 -0.007 -0.029 -0.038 -0.018  0.031  0.044      0.011    
temptrend_abs.sc:tempave_metab.sc                 0.134  0.116  0.154 -0.031 -0.012 -0.031 -0.027     -0.228    
temptrend_abs.sc:seas.sc                          0.032  0.029  0.038  0.123 -0.109 -0.027  0.207     -0.162    
temptrend_abs.sc:microclim.sc                    -0.055  0.002  0.074  0.183  0.006  0.011  0.077     -0.051    
temptrend_abs.sc:mass.sc                          0.315  0.008  0.031 -0.007 -0.007  0.044 -0.014     -0.004    
temptrend_abs.sc:speed.sc                        -0.717  0.085 -0.165 -0.064 -0.006  0.001 -0.055     -0.094    
temptrend_abs.sc:consumerfrac.sc                  0.120 -0.773 -0.069  0.022  0.005 -0.035  0.021      0.127    
temptrend_abs.sc:nspp.sc                         -0.159 -0.046 -0.749  0.118 -0.037  0.195 -0.070     -0.068    
tsign-1:thermal_bias.sc                          -0.006 -0.001 -0.035  0.001 -0.012  0.083  0.050      0.034    
tsign1:thermal_bias.sc                           -0.009 -0.024 -0.044 -0.057  0.000  0.084  0.073      0.084    
temptrend_abs.sc:npp.sc                          -0.064  0.004  0.113 -0.741  0.143 -0.043 -0.103      0.085    
temptrend_abs.sc:veg.sc                           0.010  0.011 -0.024  0.173 -0.733  0.003  0.537     -0.033    
                                                 tm_.:1 tm_.:_. tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc.
temptrend_abs.sc                                                                                   
REALMMarine                                                                                        
REALMTerrestrial                                                                                   
tsign1                                                                                             
tempave_metab.sc                                                                                   
seas.sc                                                                                            
microclim.sc                                                                                       
mass.sc                                                                                            
speed.sc                                                                                           
consumerfrac.sc                                                                                    
nspp.sc                                                                                            
npp.sc                                                                                             
veg.sc                                                                                             
duration.sc                                                                                        
temptrend_abs.sc:REALMMarine                                                                       
temptrend_abs.sc:REALMTerrestrial                                                                  
temptrend_abs.sc:tsign1                                                                            
temptrend_abs.sc:tempave_metab.sc                 0.052                                            
temptrend_abs.sc:seas.sc                         -0.021  0.109                                     
temptrend_abs.sc:microclim.sc                     0.020 -0.040   0.113                             
temptrend_abs.sc:mass.sc                          0.001  0.168   0.052            -0.043           
temptrend_abs.sc:speed.sc                        -0.035 -0.331  -0.003             0.043           
temptrend_abs.sc:consumerfrac.sc                  0.032 -0.124  -0.063            -0.027           
temptrend_abs.sc:nspp.sc                          0.029 -0.138  -0.040            -0.043           
tsign-1:thermal_bias.sc                          -0.042 -0.114   0.159             0.007           
tsign1:thermal_bias.sc                            0.001 -0.216   0.205             0.042           
temptrend_abs.sc:npp.sc                           0.003  0.030  -0.182            -0.312           
temptrend_abs.sc:veg.sc                           0.052  0.004   0.149             0.028           
                                                 tmptrnd_bs.sc:ms. tmptrnd_bs.sc:sp. tmptrnd_bs.sc:c.
temptrend_abs.sc                                                                                     
REALMMarine                                                                                          
REALMTerrestrial                                                                                     
tsign1                                                                                               
tempave_metab.sc                                                                                     
seas.sc                                                                                              
microclim.sc                                                                                         
mass.sc                                                                                              
speed.sc                                                                                             
consumerfrac.sc                                                                                      
nspp.sc                                                                                              
npp.sc                                                                                               
veg.sc                                                                                               
duration.sc                                                                                          
temptrend_abs.sc:REALMMarine                                                                         
temptrend_abs.sc:REALMTerrestrial                                                                    
temptrend_abs.sc:tsign1                                                                              
temptrend_abs.sc:tempave_metab.sc                                                                    
temptrend_abs.sc:seas.sc                                                                             
temptrend_abs.sc:microclim.sc                                                                        
temptrend_abs.sc:mass.sc                                                                             
temptrend_abs.sc:speed.sc                        -0.475                                              
temptrend_abs.sc:consumerfrac.sc                 -0.043            -0.120                            
temptrend_abs.sc:nspp.sc                          0.003             0.183             0.085          
tsign-1:thermal_bias.sc                           0.021             0.018            -0.005          
tsign1:thermal_bias.sc                            0.007             0.030             0.020          
temptrend_abs.sc:npp.sc                           0.039             0.037            -0.017          
temptrend_abs.sc:veg.sc                           0.004             0.000            -0.011          
                                                 tmptrnd_bs.sc:ns. t-1:_. ts1:_. tmptrnd_bs.sc:np.
temptrend_abs.sc                                                                                  
REALMMarine                                                                                       
REALMTerrestrial                                                                                  
tsign1                                                                                            
tempave_metab.sc                                                                                  
seas.sc                                                                                           
microclim.sc                                                                                      
mass.sc                                                                                           
speed.sc                                                                                          
consumerfrac.sc                                                                                   
nspp.sc                                                                                           
npp.sc                                                                                            
veg.sc                                                                                            
duration.sc                                                                                       
temptrend_abs.sc:REALMMarine                                                                      
temptrend_abs.sc:REALMTerrestrial                                                                 
temptrend_abs.sc:tsign1                                                                           
temptrend_abs.sc:tempave_metab.sc                                                                 
temptrend_abs.sc:seas.sc                                                                          
temptrend_abs.sc:microclim.sc                                                                     
temptrend_abs.sc:mass.sc                                                                          
temptrend_abs.sc:speed.sc                                                                         
temptrend_abs.sc:consumerfrac.sc                                                                  
temptrend_abs.sc:nspp.sc                                                                          
tsign-1:thermal_bias.sc                           0.018                                           
tsign1:thermal_bias.sc                            0.032             0.357                         
temptrend_abs.sc:npp.sc                          -0.114            -0.002  0.055                  
temptrend_abs.sc:veg.sc                           0.017             0.020 -0.012 -0.250           
                                                 tmptrnd_bs.sc:v. tmptrnd_bs.sc:d. h_.:REALM2T h_.:REALM2M t_.:-1
temptrend_abs.sc                                                                                                 
REALMMarine                                                                                                      
REALMTerrestrial                                                                                                 
tsign1                                                                                                           
tempave_metab.sc                                                                                                 
seas.sc                                                                                                          
microclim.sc                                                                                                     
mass.sc                                                                                                          
speed.sc                                                                                                         
consumerfrac.sc                                                                                                  
nspp.sc                                                                                                          
npp.sc                                                                                                           
veg.sc                                                                                                           
duration.sc                                                                                                      
temptrend_abs.sc:REALMMarine                                                                                     
temptrend_abs.sc:REALMTerrestrial                                                                                
temptrend_abs.sc:tsign1                                                                                          
temptrend_abs.sc:tempave_metab.sc                                                                                
temptrend_abs.sc:seas.sc                                                                                         
temptrend_abs.sc:microclim.sc                                                                                    
temptrend_abs.sc:mass.sc                                                                                         
temptrend_abs.sc:speed.sc                                                                                        
temptrend_abs.sc:consumerfrac.sc                                                                                 
temptrend_abs.sc:nspp.sc                                                                                         
tsign-1:thermal_bias.sc                                                                                          
tsign1:thermal_bias.sc                                                                                           
temptrend_abs.sc:npp.sc                                                                                          
temptrend_abs.sc:veg.sc                                                                                          
                                                 t_.:1: t_.:_.:REALM2T
temptrend_abs.sc                                                      
REALMMarine                                                           
REALMTerrestrial                                                      
tsign1                                                                
tempave_metab.sc                                                      
seas.sc                                                               
microclim.sc                                                          
mass.sc                                                               
speed.sc                                                              
consumerfrac.sc                                                       
nspp.sc                                                               
npp.sc                                                                
veg.sc                                                                
duration.sc                                                           
temptrend_abs.sc:REALMMarine                                          
temptrend_abs.sc:REALMTerrestrial                                     
temptrend_abs.sc:tsign1                                               
temptrend_abs.sc:tempave_metab.sc                                     
temptrend_abs.sc:seas.sc                                              
temptrend_abs.sc:microclim.sc                                         
temptrend_abs.sc:mass.sc                                              
temptrend_abs.sc:speed.sc                                             
temptrend_abs.sc:consumerfrac.sc                                      
temptrend_abs.sc:nspp.sc                                              
tsign-1:thermal_bias.sc                                               
tsign1:thermal_bias.sc                                                
temptrend_abs.sc:npp.sc                                               
temptrend_abs.sc:veg.sc                                               
 [ reached getOption("max.print") -- omitted 7 rows ]

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-6.64180587 -0.23617979 -0.02235224  0.26661608  5.42260805 

Number of Observations: 36017
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   231                  36017 
summary(modTfullJbetarem0)
Linear mixed-effects model fit by REML
 Data: trends[i2, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev     Corr  
(Intercept)      0.00722145 (Intr)
temptrend_abs.sc 0.01729128 -0.058

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept)  Residual
StdDev: 0.003744118 0.9597427

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-1.892596 
Fixed effects: Jbetatrendrem0 ~ temptrend_abs.sc * REALM + temptrend_abs.sc *      tsign + temptrend_abs.sc * tempave_metab.sc + temptrend_abs.sc *      seas.sc + temptrend_abs.sc * microclim.sc + temptrend_abs.sc *      mass.sc + temptrend_abs.sc * speed.sc + temptrend_abs.sc *      consumerfrac.sc + temptrend_abs.sc * nspp.sc + temptrend_abs.sc *      thermal_bias.sc:tsign + temptrend_abs.sc * npp.sc + temptrend_abs.sc *      veg.sc + temptrend_abs.sc * duration.sc + temptrend_abs.sc *      human_bowler.sc:REALM2 
 Correlation: 
                                                 (Intr) tmpt_. REALMM REALMT tsign1 tmpv_. ses.sc mcrcl. mss.sc
temptrend_abs.sc                                 -0.573                                                        
REALMMarine                                      -0.947  0.551                                                 
REALMTerrestrial                                 -0.752  0.385  0.701                                          
tsign1                                           -0.084  0.047  0.004  0.001                                   
tempave_metab.sc                                  0.100 -0.026 -0.084 -0.244  0.098                            
seas.sc                                          -0.131  0.077  0.178 -0.069 -0.074  0.088                     
microclim.sc                                     -0.078  0.060  0.090  0.022 -0.001 -0.183  0.138              
mass.sc                                           0.095 -0.010 -0.071 -0.043  0.001  0.089  0.091  0.007       
speed.sc                                          0.081 -0.063 -0.041 -0.088 -0.086 -0.110 -0.014  0.039 -0.493
consumerfrac.sc                                  -0.012  0.017  0.010  0.096 -0.022 -0.105 -0.031  0.020  0.012
nspp.sc                                           0.007 -0.032 -0.062 -0.038  0.049 -0.200 -0.033 -0.135 -0.054
npp.sc                                            0.025 -0.013 -0.040  0.032  0.066  0.073 -0.077 -0.140 -0.028
veg.sc                                           -0.442  0.365  0.462  0.011 -0.014 -0.003  0.073 -0.003  0.018
duration.sc                                      -0.041  0.009  0.031 -0.003 -0.144  0.129 -0.003 -0.024 -0.042
temptrend_abs.sc:REALMMarine                      0.554 -0.951 -0.570 -0.361 -0.009  0.023 -0.099 -0.066  0.000
temptrend_abs.sc:REALMTerrestrial                 0.377 -0.740 -0.350 -0.552  0.001  0.148  0.116  0.024 -0.011
temptrend_abs.sc:tsign1                           0.055 -0.127 -0.022  0.002 -0.442 -0.055  0.003 -0.006 -0.004
temptrend_abs.sc:tempave_metab.sc                -0.001  0.056 -0.009  0.132 -0.104 -0.539 -0.130  0.018 -0.066
temptrend_abs.sc:seas.sc                          0.079 -0.108 -0.106  0.104  0.050 -0.090 -0.726 -0.122 -0.063
temptrend_abs.sc:microclim.sc                     0.053 -0.078 -0.061  0.002 -0.009  0.112 -0.091 -0.780  0.012
temptrend_abs.sc:mass.sc                         -0.018  0.040  0.012 -0.009 -0.004 -0.077 -0.056  0.024 -0.577
temptrend_abs.sc:speed.sc                        -0.070  0.093  0.057  0.080  0.074  0.129  0.055 -0.019  0.240
temptrend_abs.sc:consumerfrac.sc                  0.010 -0.032 -0.009 -0.076  0.016  0.077  0.031  0.012  0.003
temptrend_abs.sc:nspp.sc                         -0.023  0.016  0.048  0.045 -0.026  0.119  0.035  0.070  0.011
tsign-1:thermal_bias.sc                           0.066 -0.022 -0.062 -0.049 -0.068  0.135 -0.240 -0.022 -0.040
tsign1:thermal_bias.sc                            0.122 -0.044 -0.126 -0.104  0.066  0.252 -0.437 -0.133 -0.017
temptrend_abs.sc:npp.sc                          -0.002  0.057  0.015 -0.054 -0.040 -0.016  0.065  0.155  0.001
temptrend_abs.sc:veg.sc                           0.351 -0.443 -0.367  0.020 -0.001 -0.016 -0.088 -0.009 -0.010
                                                 spd.sc cnsmr. nspp.s npp.sc veg.sc drtn.s t_.:REALMM t_.:REALMT
temptrend_abs.sc                                                                                                
REALMMarine                                                                                                     
REALMTerrestrial                                                                                                
tsign1                                                                                                          
tempave_metab.sc                                                                                                
seas.sc                                                                                                         
microclim.sc                                                                                                    
mass.sc                                                                                                         
speed.sc                                                                                                        
consumerfrac.sc                                  -0.089                                                         
nspp.sc                                           0.212  0.100                                                  
npp.sc                                            0.163 -0.052 -0.198                                           
veg.sc                                           -0.024 -0.018  0.023 -0.204                                    
duration.sc                                      -0.033  0.012 -0.240  0.077 -0.012                             
temptrend_abs.sc:REALMMarine                      0.039 -0.010  0.063  0.038 -0.386 -0.008                      
temptrend_abs.sc:REALMTerrestrial                 0.076 -0.073  0.049 -0.055  0.021  0.007  0.690               
temptrend_abs.sc:tsign1                           0.033  0.004 -0.025 -0.056 -0.025  0.020  0.040      0.006    
temptrend_abs.sc:tempave_metab.sc                 0.142  0.085  0.123 -0.020 -0.009 -0.054 -0.041     -0.239    
temptrend_abs.sc:seas.sc                          0.014  0.028  0.045  0.090 -0.086 -0.036  0.149     -0.173    
temptrend_abs.sc:microclim.sc                    -0.029 -0.008  0.083  0.167 -0.009  0.011  0.090     -0.058    
temptrend_abs.sc:mass.sc                          0.282  0.015  0.014 -0.015 -0.010  0.055 -0.003      0.016    
temptrend_abs.sc:speed.sc                        -0.601  0.071 -0.148 -0.068  0.004  0.014 -0.042     -0.098    
temptrend_abs.sc:consumerfrac.sc                  0.080 -0.717 -0.065  0.026  0.009 -0.023  0.019      0.109    
temptrend_abs.sc:nspp.sc                         -0.153 -0.045 -0.640  0.108 -0.028  0.167 -0.078     -0.068    
tsign-1:thermal_bias.sc                           0.027  0.006 -0.048  0.004 -0.012  0.085  0.029      0.020    
tsign1:thermal_bias.sc                            0.026 -0.030 -0.081 -0.042  0.000  0.105  0.035      0.057    
temptrend_abs.sc:npp.sc                          -0.085  0.016  0.109 -0.733  0.163 -0.056 -0.093      0.085    
temptrend_abs.sc:veg.sc                           0.006  0.013 -0.026  0.181 -0.813  0.012  0.474     -0.031    
                                                 tm_.:1 tm_.:_. tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc.
temptrend_abs.sc                                                                                   
REALMMarine                                                                                        
REALMTerrestrial                                                                                   
tsign1                                                                                             
tempave_metab.sc                                                                                   
seas.sc                                                                                            
microclim.sc                                                                                       
mass.sc                                                                                            
speed.sc                                                                                           
consumerfrac.sc                                                                                    
nspp.sc                                                                                            
npp.sc                                                                                             
veg.sc                                                                                             
duration.sc                                                                                        
temptrend_abs.sc:REALMMarine                                                                       
temptrend_abs.sc:REALMTerrestrial                                                                  
temptrend_abs.sc:tsign1                                                                            
temptrend_abs.sc:tempave_metab.sc                 0.047                                            
temptrend_abs.sc:seas.sc                         -0.018  0.080                                     
temptrend_abs.sc:microclim.sc                     0.009  0.029   0.157                             
temptrend_abs.sc:mass.sc                          0.009  0.140   0.042            -0.046           
temptrend_abs.sc:speed.sc                        -0.031 -0.308   0.027             0.032           
temptrend_abs.sc:consumerfrac.sc                  0.018 -0.106  -0.070            -0.031           
temptrend_abs.sc:nspp.sc                          0.024 -0.134  -0.035            -0.050           
tsign-1:thermal_bias.sc                          -0.046 -0.045   0.153             0.009           
tsign1:thermal_bias.sc                            0.016 -0.124   0.210             0.051           
temptrend_abs.sc:npp.sc                           0.025  0.053  -0.193            -0.305           
temptrend_abs.sc:veg.sc                           0.047 -0.014   0.141             0.056           
                                                 tmptrnd_bs.sc:ms. tmptrnd_bs.sc:sp. tmptrnd_bs.sc:c.
temptrend_abs.sc                                                                                     
REALMMarine                                                                                          
REALMTerrestrial                                                                                     
tsign1                                                                                               
tempave_metab.sc                                                                                     
seas.sc                                                                                              
microclim.sc                                                                                         
mass.sc                                                                                              
speed.sc                                                                                             
consumerfrac.sc                                                                                      
nspp.sc                                                                                              
npp.sc                                                                                               
veg.sc                                                                                               
duration.sc                                                                                          
temptrend_abs.sc:REALMMarine                                                                         
temptrend_abs.sc:REALMTerrestrial                                                                    
temptrend_abs.sc:tsign1                                                                              
temptrend_abs.sc:tempave_metab.sc                                                                    
temptrend_abs.sc:seas.sc                                                                             
temptrend_abs.sc:microclim.sc                                                                        
temptrend_abs.sc:mass.sc                                                                             
temptrend_abs.sc:speed.sc                        -0.446                                              
temptrend_abs.sc:consumerfrac.sc                 -0.050            -0.119                            
temptrend_abs.sc:nspp.sc                         -0.002             0.166             0.084          
tsign-1:thermal_bias.sc                           0.019            -0.008            -0.009          
tsign1:thermal_bias.sc                            0.013            -0.009             0.019          
temptrend_abs.sc:npp.sc                           0.047             0.029            -0.021          
temptrend_abs.sc:veg.sc                           0.005             0.012            -0.016          
                                                 tmptrnd_bs.sc:ns. t-1:_. ts1:_. tmptrnd_bs.sc:np.
temptrend_abs.sc                                                                                  
REALMMarine                                                                                       
REALMTerrestrial                                                                                  
tsign1                                                                                            
tempave_metab.sc                                                                                  
seas.sc                                                                                           
microclim.sc                                                                                      
mass.sc                                                                                           
speed.sc                                                                                          
consumerfrac.sc                                                                                   
nspp.sc                                                                                           
npp.sc                                                                                            
veg.sc                                                                                            
duration.sc                                                                                       
temptrend_abs.sc:REALMMarine                                                                      
temptrend_abs.sc:REALMTerrestrial                                                                 
temptrend_abs.sc:tsign1                                                                           
temptrend_abs.sc:tempave_metab.sc                                                                 
temptrend_abs.sc:seas.sc                                                                          
temptrend_abs.sc:microclim.sc                                                                     
temptrend_abs.sc:mass.sc                                                                          
temptrend_abs.sc:speed.sc                                                                         
temptrend_abs.sc:consumerfrac.sc                                                                  
temptrend_abs.sc:nspp.sc                                                                          
tsign-1:thermal_bias.sc                           0.017                                           
tsign1:thermal_bias.sc                            0.025             0.340                         
temptrend_abs.sc:npp.sc                          -0.114             0.006  0.081                  
temptrend_abs.sc:veg.sc                           0.013             0.012 -0.026 -0.281           
                                                 tmptrnd_bs.sc:v. tmptrnd_bs.sc:d. h_.:REALM2T h_.:REALM2M t_.:-1
temptrend_abs.sc                                                                                                 
REALMMarine                                                                                                      
REALMTerrestrial                                                                                                 
tsign1                                                                                                           
tempave_metab.sc                                                                                                 
seas.sc                                                                                                          
microclim.sc                                                                                                     
mass.sc                                                                                                          
speed.sc                                                                                                         
consumerfrac.sc                                                                                                  
nspp.sc                                                                                                          
npp.sc                                                                                                           
veg.sc                                                                                                           
duration.sc                                                                                                      
temptrend_abs.sc:REALMMarine                                                                                     
temptrend_abs.sc:REALMTerrestrial                                                                                
temptrend_abs.sc:tsign1                                                                                          
temptrend_abs.sc:tempave_metab.sc                                                                                
temptrend_abs.sc:seas.sc                                                                                         
temptrend_abs.sc:microclim.sc                                                                                    
temptrend_abs.sc:mass.sc                                                                                         
temptrend_abs.sc:speed.sc                                                                                        
temptrend_abs.sc:consumerfrac.sc                                                                                 
temptrend_abs.sc:nspp.sc                                                                                         
tsign-1:thermal_bias.sc                                                                                          
tsign1:thermal_bias.sc                                                                                           
temptrend_abs.sc:npp.sc                                                                                          
temptrend_abs.sc:veg.sc                                                                                          
                                                 t_.:1: t_.:_.:REALM2T
temptrend_abs.sc                                                      
REALMMarine                                                           
REALMTerrestrial                                                      
tsign1                                                                
tempave_metab.sc                                                      
seas.sc                                                               
microclim.sc                                                          
mass.sc                                                               
speed.sc                                                              
consumerfrac.sc                                                       
nspp.sc                                                               
npp.sc                                                                
veg.sc                                                                
duration.sc                                                           
temptrend_abs.sc:REALMMarine                                          
temptrend_abs.sc:REALMTerrestrial                                     
temptrend_abs.sc:tsign1                                               
temptrend_abs.sc:tempave_metab.sc                                     
temptrend_abs.sc:seas.sc                                              
temptrend_abs.sc:microclim.sc                                         
temptrend_abs.sc:mass.sc                                              
temptrend_abs.sc:speed.sc                                             
temptrend_abs.sc:consumerfrac.sc                                      
temptrend_abs.sc:nspp.sc                                              
tsign-1:thermal_bias.sc                                               
tsign1:thermal_bias.sc                                                
temptrend_abs.sc:npp.sc                                               
temptrend_abs.sc:veg.sc                                               
 [ reached getOption("max.print") -- omitted 7 rows ]

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-8.38434700 -0.32179127 -0.03059932  0.31554016  8.33570233 

Number of Observations: 36017
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   231                  36017 
summary(modTfullHornrem0)
Linear mixed-effects model fit by REML
 Data: trends[i3, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev     Corr  
(Intercept)      0.01556817 (Intr)
temptrend_abs.sc 0.02706589 -0.065

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.01880474 2.445174

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.327824 
Fixed effects: Horntrendrem0 ~ temptrend_abs.sc * REALM + temptrend_abs.sc *      tsign + temptrend_abs.sc * tempave_metab.sc + temptrend_abs.sc *      seas.sc + temptrend_abs.sc * microclim.sc + temptrend_abs.sc *      mass.sc + temptrend_abs.sc * speed.sc + temptrend_abs.sc *      consumerfrac.sc + temptrend_abs.sc * nspp.sc + temptrend_abs.sc *      thermal_bias.sc:tsign + temptrend_abs.sc * npp.sc + temptrend_abs.sc *      veg.sc + temptrend_abs.sc * duration.sc + temptrend_abs.sc *      human_bowler.sc:REALM2 
 Correlation: 
                                                 (Intr) tmpt_. REALMM REALMT tsign1 tmpv_. ses.sc mcrcl. mss.sc
temptrend_abs.sc                                 -0.501                                                        
REALMMarine                                      -0.954  0.479                                                 
REALMTerrestrial                                 -0.737  0.361  0.685                                          
tsign1                                           -0.065  0.046 -0.001  0.011                                   
tempave_metab.sc                                  0.101 -0.030 -0.080 -0.245  0.030                            
seas.sc                                          -0.109  0.050  0.157 -0.095 -0.088  0.103                     
microclim.sc                                     -0.067  0.042  0.076  0.017  0.014 -0.185  0.101              
mass.sc                                           0.067 -0.007 -0.044 -0.030 -0.007  0.089  0.096  0.007       
speed.sc                                          0.085 -0.062 -0.039 -0.090 -0.069 -0.078 -0.011  0.059 -0.421
consumerfrac.sc                                  -0.017  0.024  0.010  0.088  0.033 -0.120 -0.052  0.016 -0.014
nspp.sc                                           0.008 -0.037 -0.051 -0.053  0.052 -0.146  0.006 -0.090 -0.060
npp.sc                                            0.026  0.001 -0.038  0.058  0.012  0.024 -0.189 -0.215 -0.036
veg.sc                                           -0.518  0.276  0.536  0.028 -0.012  0.007  0.086  0.005  0.013
duration.sc                                      -0.021  0.012  0.029  0.021 -0.149  0.077 -0.030 -0.022 -0.027
temptrend_abs.sc:REALMMarine                      0.485 -0.949 -0.497 -0.337 -0.014  0.029 -0.069 -0.049 -0.004
temptrend_abs.sc:REALMTerrestrial                 0.322 -0.727 -0.296 -0.510 -0.003  0.161  0.123  0.019 -0.017
temptrend_abs.sc:tsign1                           0.041 -0.112 -0.010  0.003 -0.490 -0.038  0.007 -0.009  0.005
temptrend_abs.sc:tempave_metab.sc                -0.008  0.041 -0.001  0.142 -0.063 -0.500 -0.094  0.047 -0.052
temptrend_abs.sc:seas.sc                          0.059 -0.087 -0.081  0.111  0.052 -0.064 -0.641 -0.093 -0.060
temptrend_abs.sc:microclim.sc                     0.029 -0.045 -0.034  0.002 -0.024  0.103 -0.051 -0.705  0.016
temptrend_abs.sc:mass.sc                         -0.010  0.023  0.005 -0.014  0.012 -0.062 -0.054  0.020 -0.580
temptrend_abs.sc:speed.sc                        -0.047  0.100  0.033  0.075  0.048  0.099  0.041 -0.036  0.222
temptrend_abs.sc:consumerfrac.sc                  0.013 -0.035 -0.012 -0.064 -0.016  0.087  0.031  0.014  0.019
temptrend_abs.sc:nspp.sc                         -0.025  0.029  0.040  0.054 -0.019  0.077  0.011  0.047  0.009
tsign-1:thermal_bias.sc                           0.060 -0.021 -0.056 -0.059 -0.088  0.204 -0.213 -0.040 -0.045
tsign1:thermal_bias.sc                            0.103 -0.035 -0.105 -0.109  0.070  0.342 -0.321 -0.140 -0.016
temptrend_abs.sc:npp.sc                           0.010  0.023 -0.006 -0.055 -0.005 -0.012  0.086  0.158 -0.004
temptrend_abs.sc:veg.sc                           0.306 -0.426 -0.317  0.025 -0.011 -0.024 -0.102  0.009 -0.013
                                                 spd.sc cnsmr. nspp.s npp.sc veg.sc drtn.s t_.:REALMM t_.:REALMT
temptrend_abs.sc                                                                                                
REALMMarine                                                                                                     
REALMTerrestrial                                                                                                
tsign1                                                                                                          
tempave_metab.sc                                                                                                
seas.sc                                                                                                         
microclim.sc                                                                                                    
mass.sc                                                                                                         
speed.sc                                                                                                        
consumerfrac.sc                                  -0.086                                                         
nspp.sc                                           0.179  0.094                                                  
npp.sc                                            0.120 -0.037 -0.198                                           
veg.sc                                           -0.005 -0.003  0.021 -0.163                                    
duration.sc                                       0.011  0.008 -0.241  0.053 -0.006                             
temptrend_abs.sc:REALMMarine                      0.035 -0.018  0.063  0.018 -0.292 -0.016                      
temptrend_abs.sc:REALMTerrestrial                 0.082 -0.062  0.062 -0.057  0.030 -0.014  0.674               
temptrend_abs.sc:tsign1                           0.031 -0.012 -0.021 -0.035 -0.008  0.033  0.035      0.006    
temptrend_abs.sc:tempave_metab.sc                 0.118  0.079  0.088 -0.044 -0.013 -0.016 -0.027     -0.273    
temptrend_abs.sc:seas.sc                          0.013  0.019  0.025  0.125 -0.089 -0.016  0.123     -0.209    
temptrend_abs.sc:microclim.sc                    -0.040 -0.007  0.051  0.172  0.014  0.011  0.057     -0.060    
temptrend_abs.sc:mass.sc                          0.245  0.026  0.011 -0.011 -0.009  0.047  0.012      0.032    
temptrend_abs.sc:speed.sc                        -0.581  0.071 -0.127 -0.042 -0.015 -0.008 -0.043     -0.109    
temptrend_abs.sc:consumerfrac.sc                  0.074 -0.719 -0.063  0.013 -0.001 -0.021  0.023      0.094    
temptrend_abs.sc:nspp.sc                         -0.134 -0.044 -0.615  0.100 -0.021  0.150 -0.081     -0.080    
tsign-1:thermal_bias.sc                           0.004 -0.007 -0.044  0.008 -0.005  0.073  0.027      0.027    
tsign1:thermal_bias.sc                           -0.022 -0.017 -0.065 -0.057 -0.007  0.085  0.028      0.053    
temptrend_abs.sc:npp.sc                          -0.058  0.005  0.098 -0.652  0.098 -0.036 -0.052      0.088    
temptrend_abs.sc:veg.sc                           0.000  0.003 -0.021  0.158 -0.620  0.002  0.456     -0.060    
                                                 tm_.:1 tm_.:_. tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc.
temptrend_abs.sc                                                                                   
REALMMarine                                                                                        
REALMTerrestrial                                                                                   
tsign1                                                                                             
tempave_metab.sc                                                                                   
seas.sc                                                                                            
microclim.sc                                                                                       
mass.sc                                                                                            
speed.sc                                                                                           
consumerfrac.sc                                                                                    
nspp.sc                                                                                            
npp.sc                                                                                             
veg.sc                                                                                             
duration.sc                                                                                        
temptrend_abs.sc:REALMMarine                                                                       
temptrend_abs.sc:REALMTerrestrial                                                                  
temptrend_abs.sc:tsign1                                                                            
temptrend_abs.sc:tempave_metab.sc                 0.036                                            
temptrend_abs.sc:seas.sc                         -0.027  0.073                                     
temptrend_abs.sc:microclim.sc                     0.005  0.021   0.115                             
temptrend_abs.sc:mass.sc                         -0.003  0.103   0.050            -0.051           
temptrend_abs.sc:speed.sc                        -0.022 -0.285   0.012             0.040           
temptrend_abs.sc:consumerfrac.sc                  0.032 -0.099  -0.060            -0.037           
temptrend_abs.sc:nspp.sc                          0.012 -0.121  -0.028            -0.020           
tsign-1:thermal_bias.sc                          -0.029 -0.061   0.132             0.022           
tsign1:thermal_bias.sc                            0.009 -0.115   0.148             0.048           
temptrend_abs.sc:npp.sc                           0.004  0.077  -0.208            -0.321           
temptrend_abs.sc:veg.sc                           0.054  0.006   0.173             0.006           
                                                 tmptrnd_bs.sc:ms. tmptrnd_bs.sc:sp. tmptrnd_bs.sc:c.
temptrend_abs.sc                                                                                     
REALMMarine                                                                                          
REALMTerrestrial                                                                                     
tsign1                                                                                               
tempave_metab.sc                                                                                     
seas.sc                                                                                              
microclim.sc                                                                                         
mass.sc                                                                                              
speed.sc                                                                                             
consumerfrac.sc                                                                                      
nspp.sc                                                                                              
npp.sc                                                                                               
veg.sc                                                                                               
duration.sc                                                                                          
temptrend_abs.sc:REALMMarine                                                                         
temptrend_abs.sc:REALMTerrestrial                                                                    
temptrend_abs.sc:tsign1                                                                              
temptrend_abs.sc:tempave_metab.sc                                                                    
temptrend_abs.sc:seas.sc                                                                             
temptrend_abs.sc:microclim.sc                                                                        
temptrend_abs.sc:mass.sc                                                                             
temptrend_abs.sc:speed.sc                        -0.394                                              
temptrend_abs.sc:consumerfrac.sc                 -0.060            -0.116                            
temptrend_abs.sc:nspp.sc                          0.007             0.159             0.088          
tsign-1:thermal_bias.sc                           0.020             0.003             0.002          
tsign1:thermal_bias.sc                            0.019             0.011             0.014          
temptrend_abs.sc:npp.sc                           0.052             0.013             0.000          
temptrend_abs.sc:veg.sc                           0.007             0.023            -0.006          
                                                 tmptrnd_bs.sc:ns. t-1:_. ts1:_. tmptrnd_bs.sc:np.
temptrend_abs.sc                                                                                  
REALMMarine                                                                                       
REALMTerrestrial                                                                                  
tsign1                                                                                            
tempave_metab.sc                                                                                  
seas.sc                                                                                           
microclim.sc                                                                                      
mass.sc                                                                                           
speed.sc                                                                                          
consumerfrac.sc                                                                                   
nspp.sc                                                                                           
npp.sc                                                                                            
veg.sc                                                                                            
duration.sc                                                                                       
temptrend_abs.sc:REALMMarine                                                                      
temptrend_abs.sc:REALMTerrestrial                                                                 
temptrend_abs.sc:tsign1                                                                           
temptrend_abs.sc:tempave_metab.sc                                                                 
temptrend_abs.sc:seas.sc                                                                          
temptrend_abs.sc:microclim.sc                                                                     
temptrend_abs.sc:mass.sc                                                                          
temptrend_abs.sc:speed.sc                                                                         
temptrend_abs.sc:consumerfrac.sc                                                                  
temptrend_abs.sc:nspp.sc                                                                          
tsign-1:thermal_bias.sc                           0.014                                           
tsign1:thermal_bias.sc                            0.022             0.393                         
temptrend_abs.sc:npp.sc                          -0.100            -0.016  0.045                  
temptrend_abs.sc:veg.sc                           0.012             0.015 -0.016 -0.242           
                                                 tmptrnd_bs.sc:v. tmptrnd_bs.sc:d. h_.:REALM2T h_.:REALM2M t_.:-1
temptrend_abs.sc                                                                                                 
REALMMarine                                                                                                      
REALMTerrestrial                                                                                                 
tsign1                                                                                                           
tempave_metab.sc                                                                                                 
seas.sc                                                                                                          
microclim.sc                                                                                                     
mass.sc                                                                                                          
speed.sc                                                                                                         
consumerfrac.sc                                                                                                  
nspp.sc                                                                                                          
npp.sc                                                                                                           
veg.sc                                                                                                           
duration.sc                                                                                                      
temptrend_abs.sc:REALMMarine                                                                                     
temptrend_abs.sc:REALMTerrestrial                                                                                
temptrend_abs.sc:tsign1                                                                                          
temptrend_abs.sc:tempave_metab.sc                                                                                
temptrend_abs.sc:seas.sc                                                                                         
temptrend_abs.sc:microclim.sc                                                                                    
temptrend_abs.sc:mass.sc                                                                                         
temptrend_abs.sc:speed.sc                                                                                        
temptrend_abs.sc:consumerfrac.sc                                                                                 
temptrend_abs.sc:nspp.sc                                                                                         
tsign-1:thermal_bias.sc                                                                                          
tsign1:thermal_bias.sc                                                                                           
temptrend_abs.sc:npp.sc                                                                                          
temptrend_abs.sc:veg.sc                                                                                          
                                                 t_.:1: t_.:_.:REALM2T
temptrend_abs.sc                                                      
REALMMarine                                                           
REALMTerrestrial                                                      
tsign1                                                                
tempave_metab.sc                                                      
seas.sc                                                               
microclim.sc                                                          
mass.sc                                                               
speed.sc                                                              
consumerfrac.sc                                                       
nspp.sc                                                               
npp.sc                                                                
veg.sc                                                                
duration.sc                                                           
temptrend_abs.sc:REALMMarine                                          
temptrend_abs.sc:REALMTerrestrial                                     
temptrend_abs.sc:tsign1                                               
temptrend_abs.sc:tempave_metab.sc                                     
temptrend_abs.sc:seas.sc                                              
temptrend_abs.sc:microclim.sc                                         
temptrend_abs.sc:mass.sc                                              
temptrend_abs.sc:speed.sc                                             
temptrend_abs.sc:consumerfrac.sc                                      
temptrend_abs.sc:nspp.sc                                              
tsign-1:thermal_bias.sc                                               
tsign1:thermal_bias.sc                                                
temptrend_abs.sc:npp.sc                                               
temptrend_abs.sc:veg.sc                                               
 [ reached getOption("max.print") -- omitted 7 rows ]

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-5.67542034 -0.23386259 -0.02454167  0.23164547  5.75602618 

Number of Observations: 35327
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                   200                  35327 

Plots from the full models

Plot the coefficients


coefs1 <- summary(modTfullJturem0)$tTable
coefs2 <- summary(modTfullJbetarem0)$tTable
coefs3 <- summary(modTfullHornrem0)$tTable

varstoplot <- unique(c(rownames(coefs1), rownames(coefs2), rownames(coefs3)))
varstoplot <- varstoplot[which(!grepl('Intercept', varstoplot) | grepl(':', varstoplot))] # vars to plot

rows1_1 <- which(rownames(coefs1) %in% varstoplot) # rows in coefs
rows1_2 <- which(rownames(coefs2) %in% varstoplot)
rows1_3 <- which(rownames(coefs3) %in% varstoplot)
xlims <- range(c(coefs1[rows1_1,1] - coefs1[rows1_1,2], coefs1[rows1_1,1] + coefs1[rows1_1,2], 
                  coefs2[rows1_2,1] - coefs2[rows1_2,2], coefs2[rows1_2,1] + coefs2[rows1_2,2], 
                  coefs3[rows1_3,1] - coefs3[rows1_3,2], coefs3[rows1_3,1] + coefs3[rows1_3,2]))


cols <- brewer.pal(3, 'Dark2') # for Jtu, Jbeta and Horn models
pchs <- c(16, 16, 16)
offs <- c(0.1, 0, -0.1) # offset vertically for each model


par(las = 1, mai = c(0.5, 4, 0.1, 0.1))

plot(0,0, col = 'white', xlim = xlims, ylim = c(1,length(varstoplot)), yaxt='n', xlab = '', ylab ='')
axis(2, at = length(varstoplot):1, labels = varstoplot, cex.axis = 0.7)
abline(v = 0, col = 'grey', lty = 2)
abline(h = 1:length(varstoplot), col = 'grey', lty = 3)
for(i in 1:length(varstoplot)){
  if(varstoplot[i] %in% rownames(coefs1)){
    x = coefs1[rownames(coefs1) == varstoplot[i], 1]
    se = coefs1[rownames(coefs1) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[1], pch = pchs[1], col = cols[1])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[1], length(varstoplot) + 1 - i + offs[1]), col = cols[1])
  }
  if(varstoplot[i] %in% rownames(coefs2)){
    x = coefs2[rownames(coefs2) == varstoplot[i], 1]
    se = coefs2[rownames(coefs2) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[2], pch = pchs[2], col = cols[2])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[2], length(varstoplot) + 1 - i + offs[2]), col = cols[2])
  }
  if(varstoplot[i] %in% rownames(coefs3)){
    x = coefs3[rownames(coefs3) == varstoplot[i], 1]
    se = coefs3[rownames(coefs3) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[3], pch = pchs[3], col = cols[3])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[3], length(varstoplot) + 1 - i + offs[3]), col = cols[3])
  }
}
legend('topleft', col = cols, pch = 16, lwd = 1, legend = c('Jtu', 'Jbeta', 'Horn'), cex = 0.5)

Plot interactions (Jaccard turnover) without year 1

## NEED TO ALIGN THE INTS DF AGAIN

# set up the interactions to plot
ints <- data.frame(vars = c('tsign', 'tempave_metab', 'seas', 'microclim', 'mass', 'speed', 
                            'consumerfrac', 'nspp', 'thermal_bias', 'npp', 'veg', 'duration', 
                            'human_bowler', 'human_bowler'),
           min =      c(1, -10, 0.1, 0,   0,   0,   0,   0.3, -10, 1.9, 0,   0.5, 0,   0), 
           max =      c(2, 30,  16,  6,   8,   2,   1,   2.6, 10,  3.7, 1,   2,   9,   9),
           log =      c(F, F,   F,   F,   T,   T,   F,   T,   F,   T,   F,   T,   F,   F),
           len =      c(2, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100),
           discrete = c(T, F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F),
           REALM = c(rep('Freshwater', 13), 'Marine'),
           REALM2 = c(rep('TerrFresh', 13), 'Marine'),
           stringsAsFactors = FALSE)
basetab <- data.frame(tempave.sc = 0, tempave_metab.sc = 0, 
                      seas.sc = 0, microclim.sc = 0, mass.sc = 0, 
                      speed.sc = 0, lifespan.sc = 0, endothermfrac.sc = 0, 
                      nspp.sc = 0, thermal_bias.sc = 0, npp.sc = 0, human_bowler.sc = 0, veg.sc = 0,
                      consumerfrac.sc = 0, duration.sc = 0,
                      nyrBT = 20, STUDY_ID = 127L, rarefyID = '127_514668')

# make the data frames for each interaction to plot                
for(j in 1:nrow(ints)){
  # set up a grid of temperature trends and the interacting variable
  if(ints$log[j]) intvars <- list(temptrend = seq(-1.5, 1.5, length.out = 100), 
                                  new = 10^seq(ints$min[j], ints$max[j], length.out = ints$len[j]),
                                   var = ints$vars[j])
  if(!ints$log[j]) intvars <- list(temptrend = seq(-1.5, 1.5, length.out = 100), 
                                   new = seq(ints$min[j], ints$max[j], length.out = ints$len[j]),
                                   var = ints$vars[j])
  names(intvars) <- c('temptrend', ints$vars[j], 'var')
  thisdat <- expand.grid(intvars)
  
  # scale the interacting variable
  cent <- attr(trends[[paste0(ints$var[j], '.sc')]], 'scaled:center')
  scl <- attr(trends[[paste0(ints$var[j], '.sc')]], 'scaled:scale')
  if(!is.null(cent) & !is.null(scl)){
    if(ints$log[j]) thisdat[[paste0(ints$var[j], '.sc')]] <- (log(thisdat[[ints$var[j]]]) - cent)/scl
    if(!ints$log[j]) thisdat[[paste0(ints$var[j], '.sc')]] <- (thisdat[[ints$var[j]]] - cent)/scl
  }

  # merge with the rest of the columns
  if(ints$var[j] != 'tsign') colnamestouse <- setdiff(colnames(basetab), paste0(ints$var[j], '.sc'))
  if(ints$var[j] == 'tsign') colnamestouse <- setdiff(colnames(basetab), ints$var[j])
  thisdat <- cbind(thisdat, basetab[, colnamestouse])

  # add realm
  thisdat$REALM <- ints$REALM[j]
  thisdat$REALM2 <- ints$REALM2[j]
  
  # merge with the previous iterations
  if(j == 1) newdat <- thisdat
  if(j > 1){
    colstoadd <- setdiff(colnames(thisdat), colnames(newdat))
    for(toadd in colstoadd){
      newdat[[toadd]] <- NA
    }
    
    colstoadd2 <- setdiff(colnames(newdat), colnames(thisdat))
    for(toadd in colstoadd2){
      thisdat[[toadd]] <- NA
    }
    
    newdat <- rbind(newdat, thisdat)
  } 
}

# character so that new levels can be added
newdat$REALM <- as.character(newdat$REALM)
newdat$REALM2 <- as.character(newdat$REALM2)

# add extra rows so that all factor levels are represented (for predict.lme to work)
newdat <- rbind(newdat[1:4, ], newdat)
newdat$REALM[1:4] <- c('Marine', 'Marine', 'Terrestrial', 'Terrestrial')
newdat$REALM2[1:4] <- c('Marine', 'Marine', 'TerrFresh', 'TerrFresh')
newdat$temptrend[1:4] <- c(-1, 1, -1, 1)

# trim to at least some temperature change (so that tsign is -1 or 1)
newdat <- newdat[newdat$temptrend != 0,]

# scale the temperature vars
newdat$temptrend.sc <- newdat$temptrend/attr(trends$temptrend.sc, 'scaled:scale') 
newdat$temptrend_abs <- abs(newdat$temptrend)
newdat$temptrend_abs.sc <- (newdat$temptrend_abs)/attr(trends$temptrend_abs.sc, 'scaled:scale')
newdat$tsign <- factor(sign(newdat$temptrend))

# make predictions
newdat$preds <- predict(object = modTfullrem0, newdata = newdat, level = 0)

#remove the extra rows
newdat <- newdat[5:nrow(newdat), ]

# prep the plots
intplots <- vector('list', nrow(ints))
for(j in 1:length(intplots)){
  subs <- newdat$var == ints$vars[j] & newdat$temptrend > 0 # select warming side
  xvar <- 'temptrend_abs'
  title <- ints$vars[j]
  if(ints$vars[j] %in% c('tsign')){
    subs <- newdat$var == ints$vars[j]
  } 
  if(ints$vars[j] %in% c('thermal_bias')){
    subs <- newdat$var == ints$vars[j]
    xvar <- 'temptrend'
  } 
  if(ints$vars[j] %in% c('human_bowler')){
    subs <- newdat$var == ints$vars[j] & newdat$temptrend > 0 & newdat$REALM2 == ints$REALM2[j]
    title <- paste0('human:', ints$REALM2[j])
  } 

  thisplot <- ggplot(newdat[subs, ], 
                     aes_string(x = xvar, y = 'preds', 
                                group = ints$vars[j], 
                                color = ints$vars[j])) +
    geom_line() +
    coord_cartesian(ylim = c(-0.6, 0.6)) +
    theme(plot.margin = unit(c(0.5,0,0.5,0), 'cm')) +
    labs(title = title)
  if(ints$log[j] & !ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_distiller(palette = "YlGnBu", trans = 'log')
  }
  if(!ints$log[j] & !ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_distiller(palette = "YlGnBu", trans = 'identity')
  }
  if(ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_brewer(palette = "Dark2")
  }
}

#grid.arrange(grobs = intplots, '+', theme(plot.margin = unit(c(0,0,0,0), 'cm'))), ncol=2)
#do.call('grid.arrange', c(intplots, ncol = 2))
grid.arrange(grobs = intplots, ncol = 3)


# write out the interactions
write.csv(newdat, file = 'temp/interactions.csv')

Plot residuals against each predictor (Jaccard turnover)

resids <- resid(modTfull1)
preds <- getData(modTfull1)
col = '#00000033'
cex = 0.5
par(mfrow = c(5,4))
boxplot(resids ~ preds$REALM, cex = cex, col = col)
plot(preds$temptrend_abs.sc, resids, cex = cex, col = col)
plot(preds$tsign, resids, cex = cex, col = col)
plot(preds$tempave.sc, resids, cex = cex, col = col)
plot(preds$tempave_metab.sc, resids, cex = cex, col = col)
plot(preds$seas.sc, resids, cex = cex, col = col)
plot(preds$microclim.sc, resids, cex = cex, col = col)
plot(preds$mass.sc, resids, cex = cex, col = col)
plot(preds$speed.sc, resids, cex = cex, col = col)
plot(preds$lifespan.sc, resids, cex = cex, col = col)
plot(preds$consumerfrac.sc, resids, cex = cex, col = col)
plot(preds$endothermfrac.sc, resids, cex = cex, col = col)
plot(preds$nspp.sc, resids, cex = cex, col = col)
plot(preds$thermal_bias.sc, resids, cex = cex, col = col)
plot(preds$npp.sc, resids, cex = cex, col = col)
plot(preds$veg.sc, resids, cex = cex, col = col)
plot(preds$human_bowler.sc, resids, cex = cex, col = col)

Remove each term from the full model

    tryCatch({
      modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                              random = randef, weights = varef, data = trends[i,], method = 'ML')
      
    }, error = function(e){
      print('going to optim (Jtu)')
      tryCatch({
        modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                random = randef, weights = varef, data = trends[i,], method = 'ML',
                                control = lmeControl(opt = 'optim'))
        
      }, error = function(e){
        print('going to more iters (Jtu)') 
        tryCatch({
          modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                  random = randef, weights = varef, data = trends[i,], method = 'ML',
                                  control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))
          
        }, error= function(e){
          print('giving up on this one')
          modTdrops[[j+2]] <- NA
        })
      })
    })
[1] "going to optim (Jtu)"
[1] "going to more iters (Jtu)"

Plot deltaAICs for all 3 models

# transform for a plot
aicsfromfulllong <- reshape(aicsfromfull, direction = 'long',
                            varying = c('dAIC_Jtu', 'dAIC_Jbeta', 'dAIC_Horn'),
                            v.names = 'dAIC',
                            idvar = 'mod',
                            timevar = 'type',
                            times = c('Jtu', 'Jbeta', 'Horn'))

trans = function(x) sign(x)*sqrt(abs(x))
aicsfromfulllong$dAIC_tr <- trans(aicsfromfulllong$dAIC)

# plot
xlims <- range(aicsfromfulllong$dAIC_tr, na.rm = TRUE)
xticks <- c(-10, 0, 10, 100, 1000, 10000)
par(mai = c(0.5, 3, 0.1, 0.1))
with(aicsfromfulllong[aicsfromfulllong$type == 'Jtu',], plot(dAIC_tr, nrow(aicsfromfull):1, 
                                                           col = 'light grey', xlim = xlims, yaxt = 'n', ylab = '', xaxt = 'n'))
with(aicsfromfulllong[aicsfromfulllong$type == 'Jbeta',], points(dAIC_tr, nrow(aicsfromfull):1 - 0.1, col = 'dark grey'))
with(aicsfromfulllong[aicsfromfulllong$type == 'Horn',], points(dAIC_tr, nrow(aicsfromfull):1 - 0.2, col = 'black'))
axis(2, at = nrow(aicsfromfull):1, labels = aicsfromfull$mod, las = 1, cex.axis = 0.7)
axis(1, at = trans(xticks), labels = xticks, cex.axis = 0.5)
abline(v = 0, lty =2, col = 'grey')

Light grey is for Jaccard turnover, dark grey is for Jaccard total, black is for Morisita-Horn. Clear that removing temperature trend makes the model quite a bit worse and has the biggest effect.

Simplify the full models

This takes a couple days on a laptop to run.

i1 <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i2 <- trends[, complete.cases(Jbetatrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i3 <- trends[, complete.cases(Horntrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

# simplify the full models
if(file.exists('temp/modTsimpJturem0.rds')){
  modTsimpJturem0 <- readRDS('temp/modTsimpJturem0.rds')
} else {
  modTfullJturem0ML <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i1,], method = 'ML',
                       control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))
  modTsimpJturem0 <- stepAIC(modTfullJturem0ML, direction = 'backward')
  saveRDS(modTsimpJturem0, file = 'temp/modTsimpJturem0.rds')
}

if(file.exists('temp/modTsimpJbetarem0.rds')){
  modTsimpJbetarem0 <- readRDS('temp/modTsimpJbetarem0.rds')
} else {
  modTfullJbetarem0ML <- lme(Jbetatrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'ML')
  modTsimpJbetarem0 <- stepAIC(modTfullJbetarem0ML, direction = 'backward')
  saveRDS(modTsimpJbetarem0, file = 'temp/modTsimpJbetarem0.rds')
}

if(file.exists('temp/modTsimpHornrem0.rds')){
  modTsimpHornrem0 <- readRDS('temp/modTsimpHornrem0.rds')
} else {
  modTfullHornrem0ML <- lme(Horntrendrem0 ~ temptrend_abs.sc*REALM + 
                        temptrend_abs.sc*tsign +
                        temptrend_abs.sc*tempave_metab.sc + 
                        temptrend_abs.sc*seas.sc + 
                        temptrend_abs.sc*microclim.sc + 
                        temptrend_abs.sc*mass.sc + 
                        temptrend_abs.sc*speed.sc + 
                        temptrend_abs.sc*consumerfrac.sc +
                        temptrend_abs.sc*nspp.sc +
                        temptrend_abs.sc*thermal_bias.sc:tsign +
                        temptrend_abs.sc*npp.sc +
                        temptrend_abs.sc*veg.sc +
                        temptrend_abs.sc*duration.sc +
                        temptrend_abs.sc*human_bowler.sc:REALM2,
                      random = randef, weights = varef, data = trends[i3,], method = 'ML')
  modTsimpHornrem0 <- stepAIC(modTfullHornrem0ML, direction = 'backward')
  saveRDS(modTsimpHornrem0, file = 'temp/modTsimpHornrem0.rds')
}

summary(modTsimpJturem0)
summary(modTsimpJbetarem0)
summary(modTsimpHornrem0)

Make realm-specific models

summary(modTfullHornTerr)
Linear mixed-effects model fit by REML
 Data: trends[i1, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev      Corr  
(Intercept)      0.009555472 (Intr)
temptrend_abs.sc 0.022365121 0.313 

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev: 0.004029061 1.695873

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.036154 
Fixed effects: Horntrendrem0 ~ temptrend_abs.sc * tsign + temptrend_abs.sc *      tempave_metab.sc + temptrend_abs.sc * seas.sc + temptrend_abs.sc *      microclim.sc + temptrend_abs.sc * mass.sc + temptrend_abs.sc *      speed.sc + temptrend_abs.sc * consumerfrac.sc + temptrend_abs.sc *      nspp.sc + temptrend_abs.sc * thermal_bias.sc:tsign + temptrend_abs.sc *      npp.sc + temptrend_abs.sc * veg.sc + temptrend_abs.sc * duration.sc +      temptrend_abs.sc * human_bowler.sc 
 Correlation: 
                                         (Intr) tmpt_. tsign1 tmpv_. ses.sc mcrcl. mss.sc spd.sc cnsmr.
temptrend_abs.sc                         -0.629                                                        
tsign1                                   -0.215  0.019                                                 
tempave_metab.sc                         -0.486  0.307 -0.209                                          
seas.sc                                  -0.258  0.267 -0.078  0.049                                   
microclim.sc                             -0.182  0.232  0.094 -0.018  0.473                            
mass.sc                                   0.515 -0.246 -0.010 -0.141  0.008  0.039                     
speed.sc                                  0.102  0.048 -0.033 -0.595 -0.025 -0.031 -0.141              
consumerfrac.sc                           0.356 -0.340  0.181  0.004 -0.048  0.072  0.424 -0.654       
nspp.sc                                  -0.064  0.025 -0.016 -0.061 -0.118 -0.161 -0.012  0.032  0.000
npp.sc                                   -0.044  0.007  0.118 -0.045  0.204  0.149  0.011  0.013 -0.007
veg.sc                                   -0.213  0.262 -0.078 -0.082 -0.069 -0.106  0.001  0.198 -0.160
duration.sc                              -0.098  0.042 -0.186  0.067  0.069 -0.016 -0.013 -0.051  0.138
human_bowler.sc                          -0.229  0.250  0.042  0.057 -0.059  0.150 -0.120  0.007 -0.147
temptrend_abs.sc:tsign1                   0.169 -0.306 -0.490  0.093 -0.109 -0.195  0.015  0.050 -0.111
temptrend_abs.sc:tempave_metab.sc         0.296 -0.503  0.138 -0.662 -0.039 -0.003  0.002  0.427 -0.032
temptrend_abs.sc:seas.sc                  0.219 -0.380  0.028 -0.025 -0.748 -0.378 -0.012  0.008  0.045
temptrend_abs.sc:microclim.sc             0.174 -0.290 -0.097  0.007 -0.367 -0.845 -0.034  0.038 -0.071
temptrend_abs.sc:mass.sc                 -0.313  0.361  0.005 -0.015 -0.041 -0.038 -0.642  0.280 -0.433
temptrend_abs.sc:speed.sc                 0.039  0.012  0.007  0.427  0.048  0.058  0.213 -0.732  0.560
temptrend_abs.sc:consumerfrac.sc         -0.283  0.407 -0.096 -0.047 -0.009 -0.066 -0.336  0.552 -0.784
temptrend_abs.sc:nspp.sc                  0.025 -0.086  0.006  0.050  0.053  0.060 -0.005  0.003 -0.014
tsign-1:thermal_bias.sc                  -0.091 -0.062  0.667 -0.179 -0.286 -0.090  0.013 -0.024  0.140
tsign1:thermal_bias.sc                    0.212 -0.208 -0.065 -0.033 -0.666 -0.403  0.049  0.039 -0.026
temptrend_abs.sc:npp.sc                   0.027  0.023 -0.116  0.050 -0.131 -0.072 -0.023 -0.018  0.004
temptrend_abs.sc:veg.sc                   0.225 -0.308  0.055  0.044  0.027  0.048  0.031 -0.133  0.116
temptrend_abs.sc:duration.sc             -0.064  0.147  0.020 -0.086 -0.033  0.033  0.001  0.020 -0.059
temptrend_abs.sc:human_bowler.sc          0.242 -0.331 -0.037 -0.055 -0.037 -0.159  0.096  0.004  0.106
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.049 -0.049 -0.379  0.107  0.218  0.025 -0.004  0.036 -0.095
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.140  0.284  0.118 -0.001  0.450  0.297 -0.010 -0.009  0.032
                                         nspp.s npp.sc veg.sc drtn.s hmn_b. tm_.:1 tmptrnd_bs.sc:t_.
temptrend_abs.sc                                                                                    
tsign1                                                                                              
tempave_metab.sc                                                                                    
seas.sc                                                                                             
microclim.sc                                                                                        
mass.sc                                                                                             
speed.sc                                                                                            
consumerfrac.sc                                                                                     
nspp.sc                                                                                             
npp.sc                                   -0.064                                                     
veg.sc                                   -0.038 -0.553                                              
duration.sc                              -0.012 -0.020 -0.022                                       
human_bowler.sc                           0.019 -0.190  0.287 -0.127                                
temptrend_abs.sc:tsign1                  -0.013 -0.123  0.012  0.122 -0.120                         
temptrend_abs.sc:tempave_metab.sc         0.065  0.034  0.059 -0.090 -0.025 -0.077                  
temptrend_abs.sc:seas.sc                  0.073 -0.106  0.012 -0.028 -0.077  0.146  0.033           
temptrend_abs.sc:microclim.sc             0.090 -0.089  0.054  0.026 -0.139  0.232  0.001           
temptrend_abs.sc:mass.sc                  0.018 -0.026  0.020  0.031  0.088  0.053  0.063           
temptrend_abs.sc:speed.sc                -0.016 -0.007 -0.133  0.034  0.004 -0.073 -0.598           
temptrend_abs.sc:consumerfrac.sc          0.017  0.007  0.087 -0.118  0.068  0.101  0.017           
temptrend_abs.sc:nspp.sc                 -0.603  0.011  0.028 -0.007  0.055  0.019 -0.015           
tsign-1:thermal_bias.sc                   0.004  0.115 -0.079 -0.062  0.055 -0.214  0.119           
tsign1:thermal_bias.sc                   -0.028  0.143 -0.091 -0.017  0.084  0.301  0.012           
temptrend_abs.sc:npp.sc                   0.003 -0.880  0.494  0.021  0.147  0.149 -0.046           
temptrend_abs.sc:veg.sc                   0.013  0.504 -0.847  0.026 -0.301 -0.005 -0.071           
temptrend_abs.sc:duration.sc              0.152  0.001  0.010 -0.250  0.105 -0.223  0.094           
temptrend_abs.sc:human_bowler.sc          0.032  0.135 -0.334  0.116 -0.798  0.151  0.051           
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.015 -0.268  0.129  0.041 -0.116  0.635 -0.107           
temptrend_abs.sc:tsign1:thermal_bias.sc   0.026 -0.186  0.122 -0.028 -0.006 -0.369 -0.014           
                                         tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc. tmptrnd_bs.sc:ms.
temptrend_abs.sc                                                                              
tsign1                                                                                        
tempave_metab.sc                                                                              
seas.sc                                                                                       
microclim.sc                                                                                  
mass.sc                                                                                       
speed.sc                                                                                      
consumerfrac.sc                                                                               
nspp.sc                                                                                       
npp.sc                                                                                        
veg.sc                                                                                        
duration.sc                                                                                   
human_bowler.sc                                                                               
temptrend_abs.sc:tsign1                                                                       
temptrend_abs.sc:tempave_metab.sc                                                             
temptrend_abs.sc:seas.sc                                                                      
temptrend_abs.sc:microclim.sc             0.453                                               
temptrend_abs.sc:mass.sc                  0.028             0.039                             
temptrend_abs.sc:speed.sc                -0.029            -0.063            -0.439           
temptrend_abs.sc:consumerfrac.sc         -0.017             0.081             0.549           
temptrend_abs.sc:nspp.sc                 -0.055            -0.058             0.002           
tsign-1:thermal_bias.sc                   0.198             0.050             0.011           
tsign1:thermal_bias.sc                    0.496             0.298             0.021           
temptrend_abs.sc:npp.sc                   0.078             0.074             0.011           
temptrend_abs.sc:veg.sc                   0.016            -0.034            -0.028           
temptrend_abs.sc:duration.sc              0.012            -0.038            -0.011           
temptrend_abs.sc:human_bowler.sc          0.066             0.201            -0.080           
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.305            -0.022             0.054           
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.615            -0.339            -0.018           
                                         tmptrnd_bs.sc:sp. tmptrnd_bs.sc:c. tmptrnd_bs.sc:ns. t-1:_.
temptrend_abs.sc                                                                                    
tsign1                                                                                              
tempave_metab.sc                                                                                    
seas.sc                                                                                             
microclim.sc                                                                                        
mass.sc                                                                                             
speed.sc                                                                                            
consumerfrac.sc                                                                                     
nspp.sc                                                                                             
npp.sc                                                                                              
veg.sc                                                                                              
duration.sc                                                                                         
human_bowler.sc                                                                                     
temptrend_abs.sc:tsign1                                                                             
temptrend_abs.sc:tempave_metab.sc                                                                   
temptrend_abs.sc:seas.sc                                                                            
temptrend_abs.sc:microclim.sc                                                                       
temptrend_abs.sc:mass.sc                                                                            
temptrend_abs.sc:speed.sc                                                                           
temptrend_abs.sc:consumerfrac.sc         -0.699                                                     
temptrend_abs.sc:nspp.sc                 -0.032             0.012                                   
tsign-1:thermal_bias.sc                  -0.014            -0.061            0.022                  
tsign1:thermal_bias.sc                   -0.027             0.039            0.049             0.281
temptrend_abs.sc:npp.sc                   0.033            -0.034           -0.024            -0.123
temptrend_abs.sc:veg.sc                   0.147            -0.097           -0.050             0.076
temptrend_abs.sc:duration.sc             -0.015             0.043           -0.259             0.026
temptrend_abs.sc:human_bowler.sc         -0.030            -0.066           -0.053            -0.040
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.045             0.083           -0.009            -0.457
temptrend_abs.sc:tsign1:thermal_bias.sc   0.016            -0.024           -0.077            -0.196
                                         ts1:_. tmptrnd_bs.sc:np. tmptrnd_bs.sc:v. tmptrnd_bs.sc:d.
temptrend_abs.sc                                                                                   
tsign1                                                                                             
tempave_metab.sc                                                                                   
seas.sc                                                                                            
microclim.sc                                                                                       
mass.sc                                                                                            
speed.sc                                                                                           
consumerfrac.sc                                                                                    
nspp.sc                                                                                            
npp.sc                                                                                             
veg.sc                                                                                             
duration.sc                                                                                        
human_bowler.sc                                                                                    
temptrend_abs.sc:tsign1                                                                            
temptrend_abs.sc:tempave_metab.sc                                                                  
temptrend_abs.sc:seas.sc                                                                           
temptrend_abs.sc:microclim.sc                                                                      
temptrend_abs.sc:mass.sc                                                                           
temptrend_abs.sc:speed.sc                                                                          
temptrend_abs.sc:consumerfrac.sc                                                                   
temptrend_abs.sc:nspp.sc                                                                           
tsign-1:thermal_bias.sc                                                                            
tsign1:thermal_bias.sc                                                                             
temptrend_abs.sc:npp.sc                  -0.152                                                    
temptrend_abs.sc:veg.sc                   0.111 -0.609                                             
temptrend_abs.sc:duration.sc             -0.064 -0.005             0.004                           
temptrend_abs.sc:human_bowler.sc         -0.044 -0.180             0.418           -0.105          
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.267  0.311            -0.156           -0.092          
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.831  0.198            -0.155            0.085          
                                         tmptrnd_bs.sc:h_. t_.:-1
temptrend_abs.sc                                                 
tsign1                                                           
tempave_metab.sc                                                 
seas.sc                                                          
microclim.sc                                                     
mass.sc                                                          
speed.sc                                                         
consumerfrac.sc                                                  
nspp.sc                                                          
npp.sc                                                           
veg.sc                                                           
duration.sc                                                      
human_bowler.sc                                                  
temptrend_abs.sc:tsign1                                          
temptrend_abs.sc:tempave_metab.sc                                
temptrend_abs.sc:seas.sc                                         
temptrend_abs.sc:microclim.sc                                    
temptrend_abs.sc:mass.sc                                         
temptrend_abs.sc:speed.sc                                        
temptrend_abs.sc:consumerfrac.sc                                 
temptrend_abs.sc:nspp.sc                                         
tsign-1:thermal_bias.sc                                          
tsign1:thermal_bias.sc                                           
temptrend_abs.sc:npp.sc                                          
temptrend_abs.sc:veg.sc                                          
temptrend_abs.sc:duration.sc                                     
temptrend_abs.sc:human_bowler.sc                                 
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.169                  
temptrend_abs.sc:tsign1:thermal_bias.sc   0.038             0.319

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-5.32506581 -0.38770014 -0.04491269  0.36618931  5.19217938 

Number of Observations: 2299
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                    90                   2299 
summary(modTfullHornFresh)
Linear mixed-effects model fit by REML
 Data: trends[i2, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev       Corr  
(Intercept)      3.088553e-08 (Intr)
temptrend_abs.sc 2.055026e-02 0.025 

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.02085277  2.44783

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.345198 
Fixed effects: Horntrendrem0 ~ temptrend_abs.sc * tsign + temptrend_abs.sc *      tempave_metab.sc + temptrend_abs.sc * seas.sc + temptrend_abs.sc *      microclim.sc + temptrend_abs.sc * mass.sc + temptrend_abs.sc *      speed.sc + temptrend_abs.sc * nspp.sc + temptrend_abs.sc *      thermal_bias.sc:tsign + temptrend_abs.sc * npp.sc + temptrend_abs.sc *      veg.sc + temptrend_abs.sc * duration.sc + temptrend_abs.sc *      human_bowler.sc 
 Correlation: 
                                         (Intr) tmpt_. tsign1 tmpv_. ses.sc mcrcl. mss.sc spd.sc nspp.s
temptrend_abs.sc                         -0.602                                                        
tsign1                                   -0.231  0.096                                                 
tempave_metab.sc                          0.484 -0.185  0.121                                          
seas.sc                                   0.008 -0.055  0.022  0.354                                   
microclim.sc                             -0.132  0.056  0.107  0.381  0.099                            
mass.sc                                  -0.034  0.042 -0.073 -0.301  0.196 -0.393                     
speed.sc                                  0.241 -0.155  0.004  0.232  0.111  0.078 -0.670              
nspp.sc                                   0.125 -0.007 -0.163 -0.343 -0.442 -0.166  0.205 -0.078       
npp.sc                                   -0.272 -0.025  0.019 -0.132  0.452  0.147  0.096 -0.007 -0.032
veg.sc                                   -0.543  0.574 -0.027 -0.010 -0.068 -0.128 -0.108  0.065 -0.146
duration.sc                              -0.134  0.121 -0.187  0.102 -0.026  0.072 -0.112  0.023 -0.208
human_bowler.sc                           0.065  0.062 -0.078 -0.106 -0.267 -0.011  0.165 -0.105 -0.048
temptrend_abs.sc:tsign1                   0.171 -0.362 -0.534 -0.016 -0.013 -0.007 -0.046  0.047  0.081
temptrend_abs.sc:tempave_metab.sc        -0.159  0.098 -0.029 -0.614 -0.427 -0.311  0.204 -0.189  0.196
temptrend_abs.sc:seas.sc                 -0.021  0.063  0.027 -0.352 -0.697 -0.152 -0.026 -0.180  0.187
temptrend_abs.sc:microclim.sc             0.039  0.000  0.013 -0.258 -0.174 -0.622  0.213 -0.074  0.034
temptrend_abs.sc:mass.sc                 -0.016 -0.069 -0.010  0.150 -0.015  0.239 -0.674  0.446 -0.105
temptrend_abs.sc:speed.sc                -0.130  0.244  0.058 -0.123 -0.179 -0.049  0.420 -0.644  0.022
temptrend_abs.sc:nspp.sc                 -0.072 -0.009  0.157  0.177  0.134  0.083 -0.160 -0.005 -0.624
tsign-1:thermal_bias.sc                   0.429 -0.242 -0.411  0.253  0.043  0.065 -0.035  0.048  0.091
tsign1:thermal_bias.sc                    0.218 -0.069  0.138  0.581  0.367  0.124  0.040  0.015 -0.204
temptrend_abs.sc:npp.sc                   0.014  0.271  0.009 -0.067 -0.351 -0.074 -0.028 -0.023  0.022
temptrend_abs.sc:veg.sc                   0.354 -0.817  0.020 -0.011  0.016  0.060  0.023 -0.036  0.006
temptrend_abs.sc:duration.sc              0.043 -0.011 -0.037  0.044  0.153  0.060  0.045  0.061  0.117
temptrend_abs.sc:human_bowler.sc         -0.018 -0.038  0.025  0.193  0.275  0.108 -0.232  0.190  0.007
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.223  0.440  0.187 -0.198 -0.170 -0.066  0.098 -0.065  0.040
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.099  0.022 -0.106 -0.344 -0.342 -0.044 -0.075 -0.026  0.075
                                         npp.sc veg.sc drtn.s hmn_b. tm_.:1 tmptrnd_bs.sc:t_.
temptrend_abs.sc                                                                             
tsign1                                                                                       
tempave_metab.sc                                                                             
seas.sc                                                                                      
microclim.sc                                                                                 
mass.sc                                                                                      
speed.sc                                                                                     
nspp.sc                                                                                      
npp.sc                                                                                       
veg.sc                                   -0.358                                              
duration.sc                               0.041  0.119                                       
human_bowler.sc                          -0.123  0.057  0.041                                
temptrend_abs.sc:tsign1                  -0.018 -0.047  0.054 -0.032                         
temptrend_abs.sc:tempave_metab.sc        -0.151 -0.024 -0.045  0.298 -0.040                  
temptrend_abs.sc:seas.sc                 -0.353  0.010  0.000  0.322 -0.050  0.779           
temptrend_abs.sc:microclim.sc            -0.108  0.098 -0.008  0.118 -0.070  0.524           
temptrend_abs.sc:mass.sc                  0.021  0.040  0.076 -0.298  0.097 -0.376           
temptrend_abs.sc:speed.sc                -0.042 -0.028  0.001  0.200 -0.093  0.226           
temptrend_abs.sc:nspp.sc                 -0.031  0.046  0.116 -0.059 -0.111 -0.251           
tsign-1:thermal_bias.sc                   0.126 -0.256  0.003  0.055  0.244 -0.183           
tsign1:thermal_bias.sc                    0.124  0.060  0.027  0.188 -0.140 -0.399           
temptrend_abs.sc:npp.sc                  -0.690  0.340 -0.066  0.117  0.033  0.341           
temptrend_abs.sc:veg.sc                   0.244 -0.692 -0.075 -0.037  0.106  0.071           
temptrend_abs.sc:duration.sc              0.044 -0.090 -0.342 -0.061  0.021 -0.044           
temptrend_abs.sc:human_bowler.sc          0.116  0.005 -0.030 -0.709  0.056 -0.499           
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.246  0.236 -0.021  0.087 -0.477  0.423           
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.216 -0.030  0.076 -0.004  0.173  0.585           
                                         tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc. tmptrnd_bs.sc:ms.
temptrend_abs.sc                                                                              
tsign1                                                                                        
tempave_metab.sc                                                                              
seas.sc                                                                                       
microclim.sc                                                                                  
mass.sc                                                                                       
speed.sc                                                                                      
nspp.sc                                                                                       
npp.sc                                                                                        
veg.sc                                                                                        
duration.sc                                                                                   
human_bowler.sc                                                                               
temptrend_abs.sc:tsign1                                                                       
temptrend_abs.sc:tempave_metab.sc                                                             
temptrend_abs.sc:seas.sc                                                                      
temptrend_abs.sc:microclim.sc             0.397                                               
temptrend_abs.sc:mass.sc                 -0.201            -0.358                             
temptrend_abs.sc:speed.sc                 0.319             0.166            -0.674           
temptrend_abs.sc:nspp.sc                 -0.150            -0.005             0.180           
tsign-1:thermal_bias.sc                  -0.117            -0.061             0.052           
tsign1:thermal_bias.sc                   -0.320            -0.052            -0.104           
temptrend_abs.sc:npp.sc                   0.459             0.196            -0.075           
temptrend_abs.sc:veg.sc                   0.032            -0.138            -0.023           
temptrend_abs.sc:duration.sc             -0.210            -0.189            -0.003           
temptrend_abs.sc:human_bowler.sc         -0.501            -0.182             0.480           
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.374             0.182            -0.228           
temptrend_abs.sc:tsign1:thermal_bias.sc   0.520             0.087             0.003           
                                         tmptrnd_bs.sc:sp. tmptrnd_bs.sc:ns. t-1:_. ts1:_.
temptrend_abs.sc                                                                          
tsign1                                                                                    
tempave_metab.sc                                                                          
seas.sc                                                                                   
microclim.sc                                                                              
mass.sc                                                                                   
speed.sc                                                                                  
nspp.sc                                                                                   
npp.sc                                                                                    
veg.sc                                                                                    
duration.sc                                                                               
human_bowler.sc                                                                           
temptrend_abs.sc:tsign1                                                                   
temptrend_abs.sc:tempave_metab.sc                                                         
temptrend_abs.sc:seas.sc                                                                  
temptrend_abs.sc:microclim.sc                                                             
temptrend_abs.sc:mass.sc                                                                  
temptrend_abs.sc:speed.sc                                                                 
temptrend_abs.sc:nspp.sc                  0.016                                           
tsign-1:thermal_bias.sc                  -0.035            -0.028                         
tsign1:thermal_bias.sc                    0.034             0.041             0.196       
temptrend_abs.sc:npp.sc                   0.013             0.050            -0.173 -0.199
temptrend_abs.sc:veg.sc                   0.016            -0.022             0.144 -0.042
temptrend_abs.sc:duration.sc             -0.108            -0.279             0.071  0.059
temptrend_abs.sc:human_bowler.sc         -0.329             0.004            -0.006 -0.029
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.072            -0.085            -0.534 -0.177
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.045            -0.045            -0.159 -0.711
                                         tmptrnd_bs.sc:np. tmptrnd_bs.sc:v. tmptrnd_bs.sc:d.
temptrend_abs.sc                                                                            
tsign1                                                                                      
tempave_metab.sc                                                                            
seas.sc                                                                                     
microclim.sc                                                                                
mass.sc                                                                                     
speed.sc                                                                                    
nspp.sc                                                                                     
npp.sc                                                                                      
veg.sc                                                                                      
duration.sc                                                                                 
human_bowler.sc                                                                             
temptrend_abs.sc:tsign1                                                                     
temptrend_abs.sc:tempave_metab.sc                                                           
temptrend_abs.sc:seas.sc                                                                    
temptrend_abs.sc:microclim.sc                                                               
temptrend_abs.sc:mass.sc                                                                    
temptrend_abs.sc:speed.sc                                                                   
temptrend_abs.sc:nspp.sc                                                                    
tsign-1:thermal_bias.sc                                                                     
tsign1:thermal_bias.sc                                                                      
temptrend_abs.sc:npp.sc                                                                     
temptrend_abs.sc:veg.sc                  -0.513                                             
temptrend_abs.sc:duration.sc             -0.047             0.116                           
temptrend_abs.sc:human_bowler.sc         -0.145            -0.075            0.053          
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.494            -0.339           -0.057          
temptrend_abs.sc:tsign1:thermal_bias.sc   0.411             0.068           -0.056          
                                         tmptrnd_bs.sc:h_. t_.:-1
temptrend_abs.sc                                                 
tsign1                                                           
tempave_metab.sc                                                 
seas.sc                                                          
microclim.sc                                                     
mass.sc                                                          
speed.sc                                                         
nspp.sc                                                          
npp.sc                                                           
veg.sc                                                           
duration.sc                                                      
human_bowler.sc                                                  
temptrend_abs.sc:tsign1                                          
temptrend_abs.sc:tempave_metab.sc                                
temptrend_abs.sc:seas.sc                                         
temptrend_abs.sc:microclim.sc                                    
temptrend_abs.sc:mass.sc                                         
temptrend_abs.sc:speed.sc                                        
temptrend_abs.sc:nspp.sc                                         
tsign-1:thermal_bias.sc                                          
tsign1:thermal_bias.sc                                           
temptrend_abs.sc:npp.sc                                          
temptrend_abs.sc:veg.sc                                          
temptrend_abs.sc:duration.sc                                     
temptrend_abs.sc:human_bowler.sc                                 
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.153                  
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.145             0.337

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-4.25987257 -0.29069598 -0.02883191  0.25023757  5.23849247 

Number of Observations: 608
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                    18                    608 
summary(modTfullHornMar)
Linear mixed-effects model fit by REML
 Data: trends[i3, ] 

Random effects:
 Formula: ~temptrend_abs.sc | STUDY_ID
 Structure: General positive-definite, Log-Cholesky parametrization
                 StdDev     Corr  
(Intercept)      0.02167793 (Intr)
temptrend_abs.sc 0.02656924 0.035 

 Formula: ~1 | rarefyID %in% STUDY_ID
        (Intercept) Residual
StdDev:  0.02055808 2.782391

Variance function:
 Structure: Power of variance covariate
 Formula: ~nyrBT 
 Parameter estimates:
    power 
-2.432839 
Fixed effects: Horntrendrem0 ~ temptrend_abs.sc * tsign + temptrend_abs.sc *      tempave_metab.sc + temptrend_abs.sc * seas.sc + temptrend_abs.sc *      microclim.sc + temptrend_abs.sc * mass.sc + temptrend_abs.sc *      speed.sc + temptrend_abs.sc * consumerfrac.sc + temptrend_abs.sc *      nspp.sc + temptrend_abs.sc * thermal_bias.sc:tsign + temptrend_abs.sc *      npp.sc + temptrend_abs.sc * duration.sc + temptrend_abs.sc *      human_bowler.sc 
 Correlation: 
                                         (Intr) tmpt_. tsign1 tmpv_. ses.sc mcrcl. mss.sc spd.sc cnsmr.
temptrend_abs.sc                         -0.263                                                        
tsign1                                   -0.188  0.115                                                 
tempave_metab.sc                          0.102 -0.003  0.022                                          
seas.sc                                   0.185 -0.084 -0.091  0.221                                   
microclim.sc                              0.037 -0.045  0.008 -0.204  0.067                            
mass.sc                                   0.058 -0.033  0.000  0.062  0.037  0.012                     
speed.sc                                  0.153 -0.080 -0.079  0.072  0.073  0.039 -0.434              
consumerfrac.sc                           0.000  0.012  0.029 -0.068 -0.012  0.012 -0.055  0.000       
nspp.sc                                  -0.131  0.096  0.058 -0.114  0.092 -0.084 -0.105  0.204  0.112
npp.sc                                   -0.043  0.057 -0.002 -0.053 -0.345 -0.238 -0.024  0.081 -0.040
duration.sc                               0.041 -0.022 -0.137  0.053 -0.064 -0.016 -0.001 -0.016 -0.016
human_bowler.sc                          -0.030  0.030  0.005  0.098 -0.193  0.005 -0.026  0.040 -0.055
temptrend_abs.sc:tsign1                   0.089 -0.249 -0.520 -0.048 -0.009 -0.003  0.008  0.032 -0.017
temptrend_abs.sc:tempave_metab.sc        -0.031  0.078 -0.081 -0.437 -0.161  0.032 -0.019  0.058  0.040
temptrend_abs.sc:seas.sc                 -0.078  0.140  0.048 -0.144 -0.689 -0.051 -0.018 -0.038 -0.016
temptrend_abs.sc:microclim.sc            -0.022  0.066 -0.016  0.131 -0.010 -0.723  0.014 -0.028 -0.003
temptrend_abs.sc:mass.sc                  0.002  0.093  0.012 -0.038 -0.016  0.026 -0.573  0.252  0.060
temptrend_abs.sc:speed.sc                -0.031  0.176  0.047  0.026 -0.018 -0.039  0.237 -0.575  0.016
temptrend_abs.sc:consumerfrac.sc         -0.013 -0.027 -0.018  0.051  0.006  0.017  0.052  0.007 -0.719
temptrend_abs.sc:nspp.sc                  0.038 -0.174 -0.024  0.031 -0.065  0.046  0.026 -0.170 -0.064
tsign-1:thermal_bias.sc                   0.037  0.000 -0.166  0.241 -0.140 -0.031 -0.046  0.047 -0.004
tsign1:thermal_bias.sc                    0.006 -0.004  0.129  0.432 -0.171 -0.128 -0.002  0.027 -0.005
temptrend_abs.sc:npp.sc                   0.007 -0.082  0.014  0.031  0.208  0.182 -0.009 -0.036  0.014
temptrend_abs.sc:duration.sc             -0.104  0.249  0.028  0.111  0.011 -0.038  0.036  0.016  0.030
temptrend_abs.sc:human_bowler.sc          0.029 -0.029 -0.017 -0.067  0.145 -0.007  0.023 -0.033  0.133
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.012  0.010  0.028 -0.091  0.105  0.023  0.013  0.009 -0.003
temptrend_abs.sc:tsign1:thermal_bias.sc   0.006 -0.004 -0.104 -0.174  0.072  0.038  0.012  0.042 -0.019
                                         nspp.s npp.sc drtn.s hmn_b. tm_.:1 tmptrnd_bs.sc:t_.
temptrend_abs.sc                                                                             
tsign1                                                                                       
tempave_metab.sc                                                                             
seas.sc                                                                                      
microclim.sc                                                                                 
mass.sc                                                                                      
speed.sc                                                                                     
consumerfrac.sc                                                                              
nspp.sc                                                                                      
npp.sc                                   -0.237                                              
duration.sc                              -0.279  0.065                                       
human_bowler.sc                          -0.051 -0.158  0.018                                
temptrend_abs.sc:tsign1                  -0.036 -0.002  0.018 -0.006                         
temptrend_abs.sc:tempave_metab.sc         0.038  0.018  0.022 -0.044  0.092                  
temptrend_abs.sc:seas.sc                 -0.057  0.261  0.016  0.155  0.013  0.196           
temptrend_abs.sc:microclim.sc             0.052  0.180  0.004 -0.028 -0.019  0.029           
temptrend_abs.sc:mass.sc                  0.028 -0.019  0.033  0.031 -0.013  0.041           
temptrend_abs.sc:speed.sc                -0.167 -0.017  0.027 -0.043 -0.019 -0.156           
temptrend_abs.sc:consumerfrac.sc         -0.087  0.016 -0.004  0.075  0.046 -0.049           
temptrend_abs.sc:nspp.sc                 -0.643  0.144  0.179  0.044  0.032 -0.064           
tsign-1:thermal_bias.sc                  -0.055 -0.011  0.077  0.141  0.042 -0.054           
tsign1:thermal_bias.sc                   -0.059 -0.118  0.093  0.170 -0.076 -0.125           
temptrend_abs.sc:npp.sc                   0.133 -0.656 -0.036  0.097 -0.050 -0.027           
temptrend_abs.sc:duration.sc              0.253 -0.038 -0.484  0.034 -0.079 -0.042           
temptrend_abs.sc:human_bowler.sc          0.039  0.105 -0.005 -0.735  0.018  0.089           
temptrend_abs.sc:tsign-1:thermal_bias.sc  0.008 -0.003 -0.030 -0.083  0.059  0.277           
temptrend_abs.sc:tsign1:thermal_bias.sc   0.015  0.082 -0.046 -0.122  0.164  0.469           
                                         tmptrnd_bs.sc:ss. tmptrnd_bs.sc:mc. tmptrnd_bs.sc:ms.
temptrend_abs.sc                                                                              
tsign1                                                                                        
tempave_metab.sc                                                                              
seas.sc                                                                                       
microclim.sc                                                                                  
mass.sc                                                                                       
speed.sc                                                                                      
consumerfrac.sc                                                                               
nspp.sc                                                                                       
npp.sc                                                                                        
duration.sc                                                                                   
human_bowler.sc                                                                               
temptrend_abs.sc:tsign1                                                                       
temptrend_abs.sc:tempave_metab.sc                                                             
temptrend_abs.sc:seas.sc                                                                      
temptrend_abs.sc:microclim.sc             0.053                                               
temptrend_abs.sc:mass.sc                  0.005            -0.063                             
temptrend_abs.sc:speed.sc                 0.067             0.051            -0.412           
temptrend_abs.sc:consumerfrac.sc         -0.029            -0.039            -0.112           
temptrend_abs.sc:nspp.sc                  0.061            -0.014            -0.009           
tsign-1:thermal_bias.sc                   0.091             0.011             0.023           
tsign1:thermal_bias.sc                    0.065             0.049             0.017           
temptrend_abs.sc:npp.sc                  -0.379            -0.338             0.065           
temptrend_abs.sc:duration.sc             -0.046             0.044            -0.068           
temptrend_abs.sc:human_bowler.sc         -0.171            -0.022            -0.025           
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.127            -0.034            -0.014           
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.069            -0.123            -0.016           
                                         tmptrnd_bs.sc:sp. tmptrnd_bs.sc:c. tmptrnd_bs.sc:ns. t-1:_.
temptrend_abs.sc                                                                                    
tsign1                                                                                              
tempave_metab.sc                                                                                    
seas.sc                                                                                             
microclim.sc                                                                                        
mass.sc                                                                                             
speed.sc                                                                                            
consumerfrac.sc                                                                                     
nspp.sc                                                                                             
npp.sc                                                                                              
duration.sc                                                                                         
human_bowler.sc                                                                                     
temptrend_abs.sc:tsign1                                                                             
temptrend_abs.sc:tempave_metab.sc                                                                   
temptrend_abs.sc:seas.sc                                                                            
temptrend_abs.sc:microclim.sc                                                                       
temptrend_abs.sc:mass.sc                                                                            
temptrend_abs.sc:speed.sc                                                                           
temptrend_abs.sc:consumerfrac.sc         -0.042                                                     
temptrend_abs.sc:nspp.sc                  0.212             0.115                                   
tsign-1:thermal_bias.sc                  -0.015            -0.001            0.009                  
tsign1:thermal_bias.sc                   -0.004             0.002            0.000             0.358
temptrend_abs.sc:npp.sc                  -0.012            -0.003           -0.152            -0.005
temptrend_abs.sc:duration.sc             -0.044             0.024           -0.356            -0.028
temptrend_abs.sc:human_bowler.sc          0.038            -0.153           -0.057            -0.092
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.047             0.029           -0.009            -0.482
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.124             0.050           -0.033            -0.177
                                         ts1:_. tmptrnd_bs.sc:np. tmptrnd_bs.sc:d. tmptrnd_bs.sc:h_.
temptrend_abs.sc                                                                                    
tsign1                                                                                              
tempave_metab.sc                                                                                    
seas.sc                                                                                             
microclim.sc                                                                                        
mass.sc                                                                                             
speed.sc                                                                                            
consumerfrac.sc                                                                                     
nspp.sc                                                                                             
npp.sc                                                                                              
duration.sc                                                                                         
human_bowler.sc                                                                                     
temptrend_abs.sc:tsign1                                                                             
temptrend_abs.sc:tempave_metab.sc                                                                   
temptrend_abs.sc:seas.sc                                                                            
temptrend_abs.sc:microclim.sc                                                                       
temptrend_abs.sc:mass.sc                                                                            
temptrend_abs.sc:speed.sc                                                                           
temptrend_abs.sc:consumerfrac.sc                                                                    
temptrend_abs.sc:nspp.sc                                                                            
tsign-1:thermal_bias.sc                                                                             
tsign1:thermal_bias.sc                                                                              
temptrend_abs.sc:npp.sc                   0.086                                                     
temptrend_abs.sc:duration.sc             -0.055  0.054                                              
temptrend_abs.sc:human_bowler.sc         -0.124 -0.140            -0.020                            
temptrend_abs.sc:tsign-1:thermal_bias.sc -0.174  0.011             0.066            0.087           
temptrend_abs.sc:tsign1:thermal_bias.sc  -0.571 -0.084             0.037            0.137           
                                         t_.:-1
temptrend_abs.sc                               
tsign1                                         
tempave_metab.sc                               
seas.sc                                        
microclim.sc                                   
mass.sc                                        
speed.sc                                       
consumerfrac.sc                                
nspp.sc                                        
npp.sc                                         
duration.sc                                    
human_bowler.sc                                
temptrend_abs.sc:tsign1                        
temptrend_abs.sc:tempave_metab.sc              
temptrend_abs.sc:seas.sc                       
temptrend_abs.sc:microclim.sc                  
temptrend_abs.sc:mass.sc                       
temptrend_abs.sc:speed.sc                      
temptrend_abs.sc:consumerfrac.sc               
temptrend_abs.sc:nspp.sc                       
tsign-1:thermal_bias.sc                        
tsign1:thermal_bias.sc                         
temptrend_abs.sc:npp.sc                        
temptrend_abs.sc:duration.sc                   
temptrend_abs.sc:human_bowler.sc               
temptrend_abs.sc:tsign-1:thermal_bias.sc       
temptrend_abs.sc:tsign1:thermal_bias.sc   0.341

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-5.59847381 -0.21790065 -0.02487884  0.21515504  5.76491820 

Number of Observations: 32420
Number of Groups: 
              STUDY_ID rarefyID %in% STUDY_ID 
                    92                  32420 

Plot the realm-specific coefficients

Also uses the full models across all realms


coefs1 <- summary(modTfullHornrem0)$tTable
coefs2 <- summary(modTfullHornTerr)$tTable
coefs3 <- summary(modTfullHornFresh)$tTable
coefs4 <- summary(modTfullHornMar)$tTable

varstoplot <- unique(c(rownames(coefs1), rownames(coefs2), rownames(coefs3), rownames(coefs4)))
varstoplot <- varstoplot[which(!grepl('Intercept', varstoplot) | grepl(':', varstoplot))] # vars to plot

rows1_1 <- which(rownames(coefs1) %in% varstoplot) # rows in coefs
rows1_2 <- which(rownames(coefs2) %in% varstoplot)
rows1_3 <- which(rownames(coefs3) %in% varstoplot)
rows1_4 <- which(rownames(coefs4) %in% varstoplot)
xlims <- range(c(coefs1[rows1_1,1] - coefs1[rows1_1,2], coefs1[rows1_1,1] + coefs1[rows1_1,2], 
                 coefs2[rows1_2,1] - coefs2[rows1_2,2], coefs2[rows1_2,1] + coefs2[rows1_2,2], 
                 coefs3[rows1_3,1] - coefs3[rows1_3,2], coefs3[rows1_3,1] + coefs3[rows1_3,2],
                 coefs4[rows1_4,1] - coefs4[rows1_4,2], coefs4[rows1_4,1] + coefs4[rows1_4,2]))


cols <- brewer.pal(4, 'Dark2') # for full, terr, fresh, mar
pchs <- c(1, 16, 16, 16)
offs <- c(0.1, 0, -0.1, -0.2) # offset vertically for each model


par(las = 1, mai = c(0.5, 4, 0.1, 0.1))

plot(0,0, col = 'white', xlim = xlims, ylim = c(1,length(varstoplot)), yaxt='n', xlab = '', ylab ='')
axis(2, at = length(varstoplot):1, labels = varstoplot, cex.axis = 0.7)
abline(v = 0, col = 'grey', lty = 2)
abline(h = 1:length(varstoplot), col = 'grey', lty = 3)
for(i in 1:length(varstoplot)){
  if(varstoplot[i] %in% rownames(coefs1)){
    x = coefs1[rownames(coefs1) == varstoplot[i], 1]
    se = coefs1[rownames(coefs1) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[1], pch = pchs[1], col = cols[1])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[1], length(varstoplot) + 1 - i + offs[1]), col = cols[1])
  }
  if(varstoplot[i] %in% rownames(coefs2)){
    x = coefs2[rownames(coefs2) == varstoplot[i], 1]
    se = coefs2[rownames(coefs2) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[2], pch = pchs[2], col = cols[2])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[2], length(varstoplot) + 1 - i + offs[2]), col = cols[2])
  }
  if(varstoplot[i] %in% rownames(coefs3)){
    x = coefs3[rownames(coefs3) == varstoplot[i], 1]
    se = coefs3[rownames(coefs3) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[3], pch = pchs[3], col = cols[3])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[3], length(varstoplot) + 1 - i + offs[3]), col = cols[3])
  }
  if(varstoplot[i] %in% rownames(coefs4)){
    x = coefs4[rownames(coefs4) == varstoplot[i], 1]
    se = coefs4[rownames(coefs4) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[4], pch = pchs[4], col = cols[4])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[4], length(varstoplot) + 1 - i + offs[4]), col = cols[4])
  }
}
legend('bottomleft', col = cols, pch = pchs, lwd = 1, legend = c('All', 'Terestrial', 'Freshwater', 'Marine'))

[End text in hopes this helps the last figure show up when knitted]

---
title: 'Drivers of variation in the community response to temperature change across realms'
subtitle: '(using mixed effects models)'
output: 
    html_notebook: default
    #html_document: default
    github_document: default
---

Collaborators: Shane Blowes, Jon Chase, Helmut Hillebrand, Michael Burrows, Amanda Bates, Uli Brose, Benoit Gauzens, Laura Antao, Ruben Remelgado, Carsten Meyer, Myriam Hirt, maybe others
Assistance: Katherine Lew, Josef Hauser

# Introduction
- Climate change is driving a widespread reorganization of ecological communities around the world (Parmsesan & Yohe 2003, Poloczanska et al. 2013),
- but the impacts of climate change vary substantially from one location to another and among taxa (Molinos et al. 2016 NCC, Antao et al. 2020 NEE).
- Community reorganization is substantially more common than an aggregate loss or gain of species (Dornelas et al. 2014 Science, Blowes et al. 2019 Science, Hillebrand et al. 2017 J Appl Ecol)
- There are many hypotheses for why some communities are more sensitive to warming than others, including differences in
  - metabolic rates (Dillon et al. 2010 Nature), 
  - thermal physiology (Deutsch et al. 2008 PNAS, Pinsky et al. 2019 Nature), 
  - microclimate availability (Burrows et al. 2019 NCC, Suggitt et al. 2018 NCC),
  - species mobility (Poloczanska et al. 2013 NCC, Burrows et al. 2011 Science, Sunday et al. 2012 NCC)
  - or generation time (Beaugrand et al. 2009 DSR II, Poloczanska et al. 2013 NCC),
  - consumers vs. producers (Petchey et al. 1999 Nature)
  - community composition (Stuart-Smith et al. 2015 Nature, Beaugrand et a. 2015 NCC, Trisos et al. 2020 Nature), 
  - ecosystem productivity (Thomas et al. 2017 GCB, Brett 1971 Am Zoo),
  - exposure to human impacts (White & Kerr 2006 Ecography)
  - and among realms (Antao et al. 2020 NEE).
- Scaling up from organismal effects to whole ecological communities is complex, and yet these scales are critical for ecosystem functioning and human well-being. 
- There is a need for a comprehensive test to understand where warming is driving and is likely to drive the most dramatic community turnover

# Methods
- BioTime dataset, gridded to 96 km2 hexagons, summarized as temporal turnover (Blowes)
  - Temporal slope of Jaccard turnover compared to the first year (NOT including the first year compared to itself)
  - Same for Jaccard total
  - and Morisita-Horn turnover
- Explanatory variables considered for differences in rate of turnover:
  - Temperature trend over the time-frame of each time-series (CRU TS 4.03 on land and in freshwater, ERSST v5 in the ocean)
  - Seasonality as a metric of thermal sensitivity (Deutsch et al. 2008 PNAS). Standard deviation of monthly temperatures.
  - Microclimates calculated from WorldClim and BioOracle (Laura Antao)
  - Body mass, collated from databases and literature searches
  - Metabolic temperature, from average temperature if ectotherms (Dillon et al. 2010 Nature, Antao et al. 2020 Nat E&E)
  - Mobility calculated from body mass and taxonomic group classifications of mobility mode (fly, run, swim, crawl, sessile). Fly/run/swim followed the allometric relationship in Hirt et al. 2017 Nat E&E. Crawl set at 0.1 km/hr, sessile set to 0 km/hr. Then calculated averaged within each assemblage.
  - Generation time calculated from body mass and endotherm vs. ectotherm classifications, following McCoy & Gillooly 2008 ELE. Averaged across species within each assemblage. 
  - Consumer vs. producer classification by species
  - Endotherm vs. ectotherm classification by species
  - Species richness, calculated as the number of species in the assemblage
  - Net primary productivity (NPP) from the merged land/ocean product produced by the [Ocean Productivity](http://www.science.oregonstate.edu/ocean.productivity/) group at Oregon State using methods from Zhao et al. 2005 and Behrenfeld & Falkowski 1997. 
  - Human impact calculated from Bowler et al. 2020 (also try data from Venter et al. 2016 and Halpern et al. 2008)
  - Thermal bias calculated from Species Temperature Indices (Mike Burrows)
  - Vegetation cover index, calculated from %tree cover and %non-tree veg cover (latter counted as 1/2), from vegetation continuous fields (Ruben Remelgado)
- Differences in temporal turnover (response variable) modeled with a linear mixed effects model (nlme package, lme() function). See below for details.

```{r setup}
library(data.table) # for handling large datasets
library(ggplot2) # for some plotting
library(nlme) # for ME models
library(maps) # for map
library(gridExtra) # to combine ggplots together
library(grid) # to combine ggplots together
library(RColorBrewer)
library(MASS) # for stepAIC

options(width=500) # turn off most text wrapping

# tell RStudio to use project root directory as the root for this notebook. Needed since we are storing code in a separate directory.
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file()) 
```

```{r load data}
# Turnover and covariates assembled by turnover_vs_temperature_prep.Rmd
trends <- fread('output/turnover_w_covariates.csv.gz')

# set realm order
trends[, REALM := factor(REALM, levels = c('Freshwater', 'Marine', 'Terrestrial'), ordered = FALSE)]

# set up sign of temperature change
trends[, tsign := factor(sign(temptrend))]

# realm that combined Terrestrial and Freshwater, for interacting with human impact
trends[, REALM2 := REALM]
levels(trends$REALM2) = list(TerrFresh = "Freshwater", TerrFresh = "Terrestrial", Marine = "Marine")

# group Marine invertebrates/plants in with All
trends[, taxa_mod2 := taxa_mod]
trends[taxa_mod == 'Marine invertebrates/plants', taxa_mod2 := 'All']

# calculate duration
trends[, duration := maxyrBT - minyrBT + 1]

# trim to data with >= 3 yrs
trends <- trends[nyrBT >= 3, ]
```


### Log-transform some variables, then center and scale. 
``` {r center and scale}
trends[, tempave.sc := scale(tempave)]
trends[, tempave_metab.sc := scale(tempave_metab)]
trends[, seas.sc := scale(seas)]
trends[, microclim.sc := scale(log(microclim))]
trends[, temptrend.sc := scale(temptrend, center = FALSE)] # do not center
trends[, temptrend_abs.sc := scale(abs(temptrend), center = FALSE)] # do not center, so that 0 is still 0 temperature change
trends[, mass.sc := scale(log(mass_mean_weight))]
trends[, speed.sc := scale(log(speed_mean_weight+1))]
trends[, lifespan.sc := scale(log(lifespan_mean_weight))]
trends[, consumerfrac.sc := scale(consfrac)]
trends[, endothermfrac.sc := scale(endofrac)]
trends[, nspp.sc := scale(log(Nspp))]
trends[, thermal_bias.sc := scale(thermal_bias)]
trends[, npp.sc := scale(log(npp))]
trends[, veg.sc := scale(log(veg+1))]
trends[, duration.sc := scale(log(duration))]
trends[, human_bowler.sc := scale(log(human_bowler+1)), by = REALM2] # separate scaling by realm
trends[REALM2 == 'TerrFresh', human_footprint.sc := scale(log(human_venter+1))]
trends[REALM2 == 'Marine', human_footprint.sc := scale(log(human_halpern))]
```

### Examine how many data points are available
Just turnover
```{r sample size all}
cat('Overall # time-series: ', nrow(trends), '\n')
cat('# studies: ', trends[, length(unique(STUDY_ID))], '\n')
cat('Data points: ', trends[, sum(nyrBT)], '\n')
trends[, table(REALM)]
trends[, table(taxa_mod)]
trends[, table(taxa_mod, REALM)]
```

With all covariates (Bowler for human)
```{r sample size for Jaccard turnover}
# the cases we can compare
apply(trends[, .(Jtutrendrem0, REALM, tempave.sc, tempave_metab.sc, seas.sc, microclim.sc, temptrend.sc, mass.sc, speed.sc, lifespan.sc, consumerfrac.sc, endothermfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)], MARGIN = 2, FUN = function(x) sum(!is.na(x)))
i <- trends[, complete.cases(Jtutrendrem0, tempave.sc, tempave_metab.sc, seas.sc, microclim.sc, temptrend.sc, mass.sc, speed.sc, lifespan.sc, consumerfrac.sc, endothermfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)]
cat('Overall # time-series: ', sum(i), '\n')
cat('# studies: ', trends[i, length(unique(STUDY_ID))], '\n')
cat('Data points: ', trends[i, sum(nyrBT)], '\n')
trends[i, table(REALM)]
trends[i, table(taxa_mod)]
trends[i, table(taxa_mod, REALM)]
```

### Choose the variance structure for mixed effects models
Try combinations of

- variance scaled to a power of the number of years in the community time-series
- variance scaled to a power of the abs temperature trend
- random intercept for taxa_mod
- random intercept for STUDY_ID
- random slope (abs temperature trend) for taxa_mod
- random slope (abs temperature trend) for STUDY_ID
- random intercept for rarefyID (for overdispersion)

And choose the one with lowest AIC (not run: takes a long time)
```{r choose variance structure for Jacard turnover, eval = FALSE}
# fit models for variance structure
fixed <- formula(Jtutrendrem0 ~ temptrend_abs.sc*REALM +
                     temptrend_abs.sc*tsign + 
                     temptrend_abs.sc*tempave_metab.sc + 
                     temptrend_abs.sc*seas.sc + 
                     temptrend_abs.sc*microclim.sc + 
                     temptrend_abs.sc*mass.sc + 
                     temptrend_abs.sc*speed.sc + 
                     temptrend_abs.sc*consumerfrac.sc +
                     temptrend_abs.sc*nspp.sc +
                     temptrend_abs.sc*thermal_bias.sc:tsign +
                     temptrend_abs.sc*npp.sc +
                     temptrend_abs.sc*veg.sc +
                     temptrend_abs.sc*duration.sc +
                     temptrend_abs.sc*human_bowler.sc:REALM2)
i <- trends[, complete.cases(Jtutrendrem0, temptrend_abs.sc, REALM, tsign, tempave_metab.sc, seas.sc, 
                             microclim.sc, mass.sc, speed.sc, consumerfrac.sc, nspp.sc,
                             thermal_bias.sc, npp.sc, veg.sc, human_bowler.sc)]
mods <- vector('list', 0)
mods[[1]] <- gls(fixed, data = trends[i,])
mods[[2]] <- gls(fixed, data = trends[i,], weights = varPower(-0.5, ~nyrBT))
mods[[3]] <- gls(fixed, data = trends[i,], weights = varPower(0.5, ~temptrend_abs.sc))

mods[[4]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2, control = lmeControl(opt = "optim"))
mods[[5]] <- lme(fixed, data = trends[i,], random = ~1|STUDY_ID, control = lmeControl(opt = "optim"))
mods[[6]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2/STUDY_ID, control = lmeControl(opt = "optim"))
mods[[7]] <- lme(fixed, data = trends[i,], random = ~1|STUDY_ID/rarefyID, control = lmeControl(opt = "optim"))
mods[[8]] <- lme(fixed, data = trends[i,], random = ~1|taxa_mod2/STUDY_ID/rarefyID, control = lmeControl(opt = "optim"))

mods[[9]] <- lme(fixed, data = trends[i,], random = ~temptrend_abs.sc | taxa_mod)
mods[[10]] <- lme(fixed, data = trends[i,], random = list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)) # includes overdispersion. new formula so that random slope is only for study level (not enough data to extend to rarefyID).

mods[[11]] <- lme(fixed, data = trends[i,], random = list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1), weights = varPower(-0.5, ~nyrBT))
mods[[12]] <- lme(fixed, data = trends[i,], random = list(taxa_mod2 = ~ temptrend_abs.sc, STUDY_ID = ~ 1, rarefyID = ~1), weights = varPower(-0.5, ~nyrBT))

aics <- sapply(mods, AIC)
minaics <- aics - min(aics)
minaics
which.min(aics)
```
Chooses the random slopes (temptrend_abs) & intercepts for STUDY_ID, overdispersion, and variance scaled to number of years.
We haven't dealt with potential testing on the boundary issues here yet.

# Results
## Where do we have data?
```{r map}
world <- map_data('world')
ggplot(world, aes(x = long, y = lat, group = group)) +
    geom_polygon(fill = 'lightgray', color = 'white') +
    geom_point(data = trends, aes(rarefyID_x, rarefyID_y, group = REALM, color = REALM), size = 0.5, alpha = 0.4)  +
    scale_color_brewer(palette="Set1", name = 'Realm') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=16),
        axis.title=element_text(size=20)) +
  labs(x = 'Longitude (°)', y = 'Latitude (°)')
```

Mostly northern hemisphere, but spread all over. Not so much in Africa or much of Asia.




Average rates of turnover (without year 1)
```{r rates of turnover rem0}
trends[abs(temptrend) >= 0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                                sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # turnover per year for locations changing temperature
trends[abs(temptrend) < 0.1, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                               sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # not changing temperature
trends[temptrend >= 0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                           sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # warming
trends[temptrend <= -0.5, .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
                            sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # cooling

trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) < 35, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # tropics and sub-tropics
trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) >= 35 & abs(rarefyID_y) < 66.56339, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # temperate
trends[abs(temptrend) >= 0.5 & abs(rarefyID_y) >= 66.56339, 
       .(ave = mean(Jtutrendrem0, na.rm=TRUE), 
         sd = sd(Jtutrendrem0, na.rm=TRUE)/sqrt(.N))] # arctic
```



## Temperature-only model (Jtutrend, Jbetatrend, Horntrend)
```{r LME temperature only rem0}
i4 <- trends[, complete.cases(Jtutrendrem0, REALM, temptrend)]

randef <- list(STUDY_ID = ~ abs(temptrend), rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

if(file.exists('temp/modonlyTtrendrem0.rds')){
  modonlyTtrendrem0 <- readRDS('temp/modonlyTtrendrem0.rds')
} else {
  modonlyTtrendrem0 <- lme(Jtutrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i4,], method = 'REML')
  saveRDS(modonlyTtrendrem0, file = 'temp/modonlyTtrendrem0.rds')
}

i5 <- trends[, complete.cases(Jbetatrendrem0, REALM, temptrend)]
if(file.exists('temp/modonlyTtrendJbetarem0.rds')){
  modonlyTtrendJbetarem0 <- readRDS('temp/modonlyTtrendJbetarem0.rds')
} else {
  modonlyTtrendJbetarem0 <- lme(Jbetatrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i5,], method = 'REML', 
                   control=lmeControl(msMaxIter = 100, maxIter = 100))
  saveRDS(modonlyTtrendJbetarem0, file = 'temp/modonlyTtrendJbetarem0.rds')
}

i6 <- trends[, complete.cases(Horntrendrem0, REALM, temptrend)]
if(file.exists('temp/modonlyTtrendHornrem0.rds')){
  modonlyTtrendHornrem0 <- readRDS('temp/modonlyTtrendHornrem0.rds')
} else {
  modonlyTtrendHornrem0 <- lme(Horntrendrem0 ~ abs(temptrend)*REALM,
                   random = randef, weights = varef, data = trends[i6,], method = 'REML')
  saveRDS(modonlyTtrendHornrem0, file = 'temp/modonlyTtrendHornrem0.rds')
}

summary(modonlyTtrendrem0)
summary(modonlyTtrendJbetarem0)
summary(modonlyTtrendHornrem0)


```

### Plot the temp-only coefficients
```{r modonlyTtrendsimp coefs}
colors <- brewer.pal(3, 'Dark2')

# make table of coefficients
coefs1 <- as.data.frame(summary(modonlyTtrendrem0)$tTable)
coefs2 <- as.data.frame(summary(modonlyTtrendJbetarem0)$tTable)
coefs3 <- as.data.frame(summary(modonlyTtrendHornrem0)$tTable)
coefs1$mod <- 'Jtu'
coefs2$mod <- 'Jbeta'
coefs3$mod <- 'Horn'
rows1 <- which(grepl('temptrend', rownames(coefs1))) # extract temperature effect
cols <- c('Value', 'Std.Error', 'mod')
allcoefs <- rbind(coefs1[rows1, cols], coefs2[rows1, cols], coefs3[rows1, cols])
allcoefs$Value[grepl('REALMMarine', rownames(allcoefs))] <- 
  allcoefs$Value[grepl('REALMMarine', rownames(allcoefs))] + 
  allcoefs$Value[!grepl('REALM', rownames(allcoefs))] # add intercept to marine effects
allcoefs$Value[grepl('REALMTerrestrial', rownames(allcoefs))] <- 
  allcoefs$Value[grepl('REALMTerrestrial', rownames(allcoefs))] + 
  allcoefs$Value[!grepl('REALM', rownames(allcoefs))] # add intercept to terrestrial effects

allcoefs$lCI <- allcoefs$Value - allcoefs$Std.Error # lower confidence interval
allcoefs$uCI <- allcoefs$Value + allcoefs$Std.Error
allcoefs$y <- c(3, 2, 1) + rep(c(0, -0.1, -0.2), c(3, 3, 3)) # y-values
allcoefs$col <- c(rep(colors[1], 3), rep(colors[2], 3), rep(colors[3], 3))
allcoefs$realm <- rep(c('Freshwater', 'Marine', 'Terrestrial'), 3)

par(las = 1, mai = c(0.8, 2, 0.1, 0.1))
plot(0,0, col = 'white', xlim=c(-0.1, 0.85), ylim = c(0.5,3), 
     yaxt='n', xlab = 'Turnover per |°C/yr|', ylab ='')
axis(2, at = 3:1, labels = c('Freshwater', 'Marine', 'Terrestrial'), cex.axis = 0.7)
abline(v = 0, col = 'grey')
for(i in 1:nrow(allcoefs)){
  with(allcoefs[i, ], points(Value, y, pch = 16, col = col))
  with(allcoefs[i, ], lines(x = c(lCI, uCI), y = c(y, y), col = col))
}
legend('bottomright', col = colors, lwd = 1, pch = 16, 
       legend = c('Jaccard turnover', 'Jaccard total', 'Horn-Morisita',
                  'Jaccard turnover rem0', 'Jaccard total rem0', 'Horn-Morisita rem0'))

```

### Nicer plots of turnover vs. temperature data
Violin plots
```{r turnover vs. temperature big plot, fig.width=6, fig.height=4.5}
# on macbook: fig.width=3, fig.height=2.375, fig.retina=3, out.width=3, out.height=2.375
# on external monitor: fig.width=6, fig.height=4.5
trends[temptrend <= -0.7, temptrendtext := 'Cooling']
trends[abs(temptrend) <= 0.05, temptrendtext := 'Stable']
trends[temptrend >= 0.7, temptrendtext := 'Warming']

trends[abs(rarefyID_y) < 35, latzone := 'Subtropics']
trends[abs(rarefyID_y) >= 35 & abs(rarefyID_x) < 66.56339, latzone := 'Temperate'] 
trends[abs(rarefyID_y) >= 66.56339, latzone := 'Polar']

p1 <- ggplot(trends[!is.na(temptrendtext), ], aes(temptrendtext, Horntrendrem0)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), fill = 'grey') +
  labs(x = '', y = 'Turnover', tag = 'A', title = 'Rate of temperature change') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=8),
        axis.title=element_text(size=10))

p2 <- ggplot(trends[abs(temptrend) >= 0.1 & !is.na(latzone), ], aes(latzone, Horntrendrem0)) +
  geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), fill = 'grey') + 
  labs(x = '', y = '', tag = 'C', title = 'Warming regions') +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank(), axis.line = element_line(colour = "black"),
        legend.key=element_blank(),
        axis.text=element_text(size=7),
        axis.title=element_text(size=10))


grid.arrange(p1, p2, ncol = 2)
```

## Full models
Try static covariates plus interactions of abs temperature trend with each covariate:

- realm
- speed
- mass
- average metabolic temperature
- consumer fraction
- environmental temperature
- seasonality
- microclimates
- thermal bias
- NPP
- vegetation
- duration
- human footprint

Except for thermal bias: interact with temperature trend (not abs)

### Fit full models
#### Bowler vs Venter/Halpern human impact
Bowler has lower AIC.
```{r LME Jacard turnover temperature full rem0}
# using Bowler for human impact
i1 <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc, human_footprint.sc)]

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

modTfullbowlerrem0 <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM +
                     temptrend_abs.sc*tsign + 
                     temptrend_abs.sc*tempave_metab.sc + 
                     temptrend_abs.sc*seas.sc + 
                     temptrend_abs.sc*microclim.sc + 
                     temptrend_abs.sc*mass.sc + 
                     temptrend_abs.sc*speed.sc + 
                     temptrend_abs.sc*consumerfrac.sc +
                     temptrend_abs.sc*nspp.sc +
                     temptrend_abs.sc*thermal_bias.sc:tsign +
                     temptrend_abs.sc*npp.sc +
                     temptrend_abs.sc*veg.sc +
                     temptrend_abs.sc*duration.sc +
                     temptrend_abs.sc*human_bowler.sc:REALM2,
                   random = randef, weights = varef, data = trends[i1,], method = 'REML')

# using Venter/Halpern for human impact
modTfullfootprintrem0 <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM + 
                     temptrend_abs.sc*tsign + 
                     temptrend_abs.sc*tempave_metab.sc + 
                     temptrend_abs.sc*seas.sc + 
                     temptrend_abs.sc*microclim.sc + 
                     temptrend_abs.sc*mass.sc + 
                     temptrend_abs.sc*speed.sc + 
                     temptrend_abs.sc*consumerfrac.sc +
                     temptrend_abs.sc*nspp.sc +
                     temptrend_abs.sc*thermal_bias.sc:tsign +
                     temptrend_abs.sc*npp.sc +
                     temptrend_abs.sc*veg.sc +
                     temptrend_abs.sc*duration.sc +
                     temptrend_abs.sc*human_footprint.sc:REALM2,
                   random = randef, weights = varef, data = trends[i1,], method = 'REML',
                   control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))

AIC(modTfullbowlerrem0, modTfullfootprintrem0)

```

#### Full models
```{r LME Jacard total and MH models rem0, fig.width=10, fig.height=8}
i1 <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i2 <- trends[, complete.cases(Jbetatrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i3 <- trends[, complete.cases(Horntrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

# full models
if(file.exists('temp/modTfullJturem0.rds')){
  modTfullJturem0 <- readRDS('temp/modTfullJturem0.rds')
} else {
  modTfullJturem0 <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'REML')
  saveRDS(modTfullJturem0, file = 'temp/modTfullJturem0.rds')
}

if(file.exists('temp/modTfullJbetarem0.rds')){
  modTfullJbetarem0 <- readRDS('temp/modTfullJbetarem0.rds')
} else {
  modTfullJbetarem0 <- lme(Jbetatrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'REML')
  saveRDS(modTfullJbetarem0, file = 'temp/modTfullJbetarem0.rds')
}

if(file.exists('temp/modTfullHornrem0.rds')){
  modTfullHornrem0 <- readRDS('temp/modTfullHornrem0.rds')
} else {
  modTfullHornrem0 <- lme(Horntrendrem0 ~ temptrend_abs.sc*REALM + 
                        temptrend_abs.sc*tsign +
                        temptrend_abs.sc*tempave_metab.sc + 
                        temptrend_abs.sc*seas.sc + 
                        temptrend_abs.sc*microclim.sc + 
                        temptrend_abs.sc*mass.sc + 
                        temptrend_abs.sc*speed.sc + 
                        temptrend_abs.sc*consumerfrac.sc +
                        temptrend_abs.sc*nspp.sc +
                        temptrend_abs.sc*thermal_bias.sc:tsign +
                        temptrend_abs.sc*npp.sc +
                        temptrend_abs.sc*veg.sc +
                        temptrend_abs.sc*duration.sc +
                        temptrend_abs.sc*human_bowler.sc:REALM2,
                      random = randef, weights = varef, data = trends[i3,], method = 'REML')
  saveRDS(modTfullHornrem0, file = 'temp/modTfullHornrem0.rds')
}

summary(modTfullJturem0)
summary(modTfullJbetarem0)
summary(modTfullHornrem0)
```

### Plots from the full models
#### Plot the coefficients
```{r plot fullTmods, fig.height=12, fig.width=9}

coefs1 <- summary(modTfullJturem0)$tTable
coefs2 <- summary(modTfullJbetarem0)$tTable
coefs3 <- summary(modTfullHornrem0)$tTable

varstoplot <- unique(c(rownames(coefs1), rownames(coefs2), rownames(coefs3)))
varstoplot <- varstoplot[which(!grepl('Intercept', varstoplot) | grepl(':', varstoplot))] # vars to plot

rows1_1 <- which(rownames(coefs1) %in% varstoplot) # rows in coefs
rows1_2 <- which(rownames(coefs2) %in% varstoplot)
rows1_3 <- which(rownames(coefs3) %in% varstoplot)
xlims <- range(c(coefs1[rows1_1,1] - coefs1[rows1_1,2], coefs1[rows1_1,1] + coefs1[rows1_1,2], 
                  coefs2[rows1_2,1] - coefs2[rows1_2,2], coefs2[rows1_2,1] + coefs2[rows1_2,2], 
                  coefs3[rows1_3,1] - coefs3[rows1_3,2], coefs3[rows1_3,1] + coefs3[rows1_3,2]))


cols <- brewer.pal(3, 'Dark2') # for Jtu, Jbeta and Horn models
pchs <- c(16, 16, 16)
offs <- c(0.1, 0, -0.1) # offset vertically for each model


par(las = 1, mai = c(0.5, 4, 0.1, 0.1))

plot(0,0, col = 'white', xlim = xlims, ylim = c(1,length(varstoplot)), yaxt='n', xlab = '', ylab ='')
axis(2, at = length(varstoplot):1, labels = varstoplot, cex.axis = 0.7)
abline(v = 0, col = 'grey', lty = 2)
abline(h = 1:length(varstoplot), col = 'grey', lty = 3)
for(i in 1:length(varstoplot)){
  if(varstoplot[i] %in% rownames(coefs1)){
    x = coefs1[rownames(coefs1) == varstoplot[i], 1]
    se = coefs1[rownames(coefs1) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[1], pch = pchs[1], col = cols[1])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[1], length(varstoplot) + 1 - i + offs[1]), col = cols[1])
  }
  if(varstoplot[i] %in% rownames(coefs2)){
    x = coefs2[rownames(coefs2) == varstoplot[i], 1]
    se = coefs2[rownames(coefs2) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[2], pch = pchs[2], col = cols[2])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[2], length(varstoplot) + 1 - i + offs[2]), col = cols[2])
  }
  if(varstoplot[i] %in% rownames(coefs3)){
    x = coefs3[rownames(coefs3) == varstoplot[i], 1]
    se = coefs3[rownames(coefs3) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[3], pch = pchs[3], col = cols[3])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[3], length(varstoplot) + 1 - i + offs[3]), col = cols[3])
  }
}
legend('topleft', col = cols, pch = 16, lwd = 1, legend = c('Jtu', 'Jbeta', 'Horn'), cex = 0.5)
```





#### Plot interactions (Jaccard turnover) without year 1
```{r interaction plots modTfullrem0, fig.height = 13, fig.width = 9}

# set up the interactions to plot
ints <- data.frame(vars = c('tsign', 'tempave_metab', 'seas', 'microclim', 'mass', 'speed', 
                            'consumerfrac', 'nspp', 'thermal_bias', 'npp', 'veg', 'duration', 
                            'human_bowler', 'human_bowler'),
           min =      c(1, -10, 0.1, 0,   0,   0,   0,   0.3, -10, 1.9, 0,   0.5, 0,   0), 
           max =      c(2, 30,  16,  6,   8,   2,   1,   2.6, 10,  3.7, 1,   2,   9,   9),
           log =      c(F, F,   F,   F,   T,   T,   F,   T,   F,   T,   F,   T,   F,   F),
           len =      c(2, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100),
           discrete = c(T, F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F,   F),
           REALM = c(rep('Freshwater', 13), 'Marine'),
           REALM2 = c(rep('TerrFresh', 13), 'Marine'),
           stringsAsFactors = FALSE)
basetab <- data.frame(tempave.sc = 0, tempave_metab.sc = 0, 
                      seas.sc = 0, microclim.sc = 0, mass.sc = 0, 
                      speed.sc = 0, lifespan.sc = 0, endothermfrac.sc = 0, 
                      nspp.sc = 0, thermal_bias.sc = 0, npp.sc = 0, human_bowler.sc = 0, veg.sc = 0,
                      consumerfrac.sc = 0, duration.sc = 0,
                      nyrBT = 20, STUDY_ID = 127L, rarefyID = '127_514668')

# make the data frames for each interaction to plot                
for(j in 1:nrow(ints)){
  # set up a grid of temperature trends and the interacting variable
  if(ints$log[j]) intvars <- list(temptrend = seq(-1.5, 1.5, length.out = 100), 
                                  new = 10^seq(ints$min[j], ints$max[j], length.out = ints$len[j]),
                                   var = ints$vars[j])
  if(!ints$log[j]) intvars <- list(temptrend = seq(-1.5, 1.5, length.out = 100), 
                                   new = seq(ints$min[j], ints$max[j], length.out = ints$len[j]),
                                   var = ints$vars[j])
  names(intvars) <- c('temptrend', ints$vars[j], 'var')
  thisdat <- expand.grid(intvars)
  
  # scale the interacting variable
  cent <- attr(trends[[paste0(ints$var[j], '.sc')]], 'scaled:center')
  scl <- attr(trends[[paste0(ints$var[j], '.sc')]], 'scaled:scale')
  if(!is.null(cent) & !is.null(scl)){
    if(ints$log[j]) thisdat[[paste0(ints$var[j], '.sc')]] <- (log(thisdat[[ints$var[j]]]) - cent)/scl
    if(!ints$log[j]) thisdat[[paste0(ints$var[j], '.sc')]] <- (thisdat[[ints$var[j]]] - cent)/scl
  }

  # merge with the rest of the columns
  if(ints$var[j] != 'tsign') colnamestouse <- setdiff(colnames(basetab), paste0(ints$var[j], '.sc'))
  if(ints$var[j] == 'tsign') colnamestouse <- setdiff(colnames(basetab), ints$var[j])
  thisdat <- cbind(thisdat, basetab[, colnamestouse])

  # add realm
  thisdat$REALM <- ints$REALM[j]
  thisdat$REALM2 <- ints$REALM2[j]
  
  # merge with the previous iterations
  if(j == 1) newdat <- thisdat
  if(j > 1){
    colstoadd <- setdiff(colnames(thisdat), colnames(newdat))
    for(toadd in colstoadd){
      newdat[[toadd]] <- NA
    }
    
    colstoadd2 <- setdiff(colnames(newdat), colnames(thisdat))
    for(toadd in colstoadd2){
      thisdat[[toadd]] <- NA
    }
    
    newdat <- rbind(newdat, thisdat)
  } 
}

# character so that new levels can be added
newdat$REALM <- as.character(newdat$REALM)
newdat$REALM2 <- as.character(newdat$REALM2)

# add extra rows so that all factor levels are represented (for predict.lme to work)
newdat <- rbind(newdat[1:4, ], newdat)
newdat$REALM[1:4] <- c('Marine', 'Marine', 'Terrestrial', 'Terrestrial')
newdat$REALM2[1:4] <- c('Marine', 'Marine', 'TerrFresh', 'TerrFresh')
newdat$temptrend[1:4] <- c(-1, 1, -1, 1)

# trim to at least some temperature change (so that tsign is -1 or 1)
newdat <- newdat[newdat$temptrend != 0,]

# scale the temperature vars
newdat$temptrend.sc <- newdat$temptrend/attr(trends$temptrend.sc, 'scaled:scale') 
newdat$temptrend_abs <- abs(newdat$temptrend)
newdat$temptrend_abs.sc <- (newdat$temptrend_abs)/attr(trends$temptrend_abs.sc, 'scaled:scale')
newdat$tsign <- factor(sign(newdat$temptrend))

# make predictions
newdat$preds <- predict(object = modTfullrem0, newdata = newdat, level = 0)

#remove the extra rows
newdat <- newdat[5:nrow(newdat), ]

# prep the plots
intplots <- vector('list', nrow(ints))
for(j in 1:length(intplots)){
  subs <- newdat$var == ints$vars[j] & newdat$temptrend > 0 # select warming side
  xvar <- 'temptrend_abs'
  title <- ints$vars[j]
  if(ints$vars[j] %in% c('tsign')){
    subs <- newdat$var == ints$vars[j]
  } 
  if(ints$vars[j] %in% c('thermal_bias')){
    subs <- newdat$var == ints$vars[j]
    xvar <- 'temptrend'
  } 
  if(ints$vars[j] %in% c('human_bowler')){
    subs <- newdat$var == ints$vars[j] & newdat$temptrend > 0 & newdat$REALM2 == ints$REALM2[j]
    title <- paste0('human:', ints$REALM2[j])
  } 

  thisplot <- ggplot(newdat[subs, ], 
                     aes_string(x = xvar, y = 'preds', 
                                group = ints$vars[j], 
                                color = ints$vars[j])) +
    geom_line() +
    coord_cartesian(ylim = c(-0.6, 0.6)) +
    theme(plot.margin = unit(c(0.5,0,0.5,0), 'cm')) +
    labs(title = title)
  if(ints$log[j] & !ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_distiller(palette = "YlGnBu", trans = 'log')
  }
  if(!ints$log[j] & !ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_distiller(palette = "YlGnBu", trans = 'identity')
  }
  if(ints$discrete[j]){
    intplots[[j]] <- thisplot + scale_color_brewer(palette = "Dark2")
  }
}

#grid.arrange(grobs = intplots, '+', theme(plot.margin = unit(c(0,0,0,0), 'cm'))), ncol=2)
#do.call('grid.arrange', c(intplots, ncol = 2))
grid.arrange(grobs = intplots, ncol = 3)

# write out the interactions
write.csv(newdat, file = 'temp/interactions.csv')

```

#### Plot residuals against each predictor (Jaccard turnover)
```{r resids modTfull1, fig.height = 10, fig.width=10}
resids <- resid(modTfull1)
preds <- getData(modTfull1)
col = '#00000033'
cex = 0.5
par(mfrow = c(5,4))
boxplot(resids ~ preds$REALM, cex = cex, col = col)
plot(preds$temptrend_abs.sc, resids, cex = cex, col = col)
plot(preds$tsign, resids, cex = cex, col = col)
plot(preds$tempave.sc, resids, cex = cex, col = col)
plot(preds$tempave_metab.sc, resids, cex = cex, col = col)
plot(preds$seas.sc, resids, cex = cex, col = col)
plot(preds$microclim.sc, resids, cex = cex, col = col)
plot(preds$mass.sc, resids, cex = cex, col = col)
plot(preds$speed.sc, resids, cex = cex, col = col)
plot(preds$lifespan.sc, resids, cex = cex, col = col)
plot(preds$consumerfrac.sc, resids, cex = cex, col = col)
plot(preds$endothermfrac.sc, resids, cex = cex, col = col)
plot(preds$nspp.sc, resids, cex = cex, col = col)
plot(preds$thermal_bias.sc, resids, cex = cex, col = col)
plot(preds$npp.sc, resids, cex = cex, col = col)
plot(preds$veg.sc, resids, cex = cex, col = col)
plot(preds$human_bowler.sc, resids, cex = cex, col = col)
```

### Remove each term from the full model
```{r term deletion from modTfull}
AICnas <- function(x){
  if(class(x) == 'NULL'){
    return(NA)
  } else {
    return(AIC(x))
  }
}

if(file.exists('output/aics_from_full.csv')){
  aicsfromfull <- read.csv('output/aics_from_full.csv')
  
  if('dAIC_Jtu' %in% colnames(aicsfromfull)){
    runJtu <- FALSE
  } else {
    runJtu <- TRUE
  }
  
  if('dAIC_Jbeta' %in% colnames(aicsfromfull)){
    runJbeta <- FALSE
  } else {
    runJbeta <- TRUE
  }
  
  if('dAIC_Horn' %in% colnames(aicsfromfull)){
    runHorn <- FALSE
  } else {
    runHorn <- TRUE
  }
  
} else {
  runJtu <- TRUE
  runJbeta <- TRUE
  runHorn <- TRUE
}

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

terms <- c('temptrend_abs.sc*REALM', 
           'temptrend_abs.sc*tsign',
           'temptrend_abs.sc*tempave_metab.sc',
           'temptrend_abs.sc*seas.sc',
           'temptrend_abs.sc*microclim.sc',
           'temptrend_abs.sc*mass.sc',
           'temptrend_abs.sc*speed.sc', 
           'temptrend_abs.sc*consumerfrac.sc',
           'temptrend_abs.sc*nspp.sc',
           'temptrend_abs.sc*thermal_bias.sc:tsign',
           'temptrend_abs.sc*npp.sc',
           'temptrend_abs.sc*veg.sc',
           'temptrend_abs.sc*duration.sc',
           'temptrend_abs.sc*human_bowler.sc:REALM2')


if(runJtu){
  i <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                               temptrend_abs.sc, mass.sc, speed.sc, 
                               consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                               veg.sc, duration.sc, human_bowler.sc)]
  
  modTdrops <- vector('list', length(terms)+2)
  names(modTdrops) <- c('full', '-temptrend_abs.sc', paste0('-', terms))
  
  # fit full model with ML for model comparison
  modTdrops[[1]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms, collapse = ' + '))),
                        random = randef, weights = varef, data = trends[i,], method = 'ML')
  
  # w/out temptrend
  modTdrops[[2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(gsub('temptrend_abs.sc\\*', '', terms), collapse = ' + '))),
                        random = list(STUDY_ID = ~ 1, rarefyID = ~1), weights = varef, data = trends[i,], method = 'ML')
  
  for(j in 1:length(terms)){
    print(j)
    tryCatch({
      modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                              random = randef, weights = varef, data = trends[i,], method = 'ML')
      
    }, error = function(e){
      print('going to optim (Jtu)')
      tryCatch({
        modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                random = randef, weights = varef, data = trends[i,], method = 'ML',
                                control = lmeControl(opt = 'optim'))
        
      }, error = function(e){
        print('going to more iters (Jtu)') 
        tryCatch({
          modTdrops[[j+2]] <- lme(formula(paste0('Jtutrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                  random = randef, weights = varef, data = trends[i,], method = 'ML',
                                  control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))
          
        }, error= function(e){
          print('giving up on this one')
          modTdrops[[j+2]] <- NA
        })
      })
    })
  }
  
  aicsJtu <- sapply(modTdrops, AICnas)
}


if(runJbeta){
  i <- trends[, complete.cases(Jbetatrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                               temptrend_abs.sc, mass.sc, speed.sc, 
                               consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                               veg.sc, duration.sc, human_bowler.sc)]
  
  modTJbetadrops <- vector('list', length(terms)+2)
  names(modTJbetadrops) <- c('full', '-temptrend_abs.sc', paste0('-', terms))
  
  # fit full model with ML for model comparison
  modTJbetadrops[[1]] <- lme(formula(paste0('Jbetatrendrem0 ~ ', paste(terms, collapse = ' + '))),
                             random = randef, weights = varef, data = trends[i,], method = 'ML')
  
  # w/out temptrend
  modTJbetadrops[[2]] <- lme(formula(paste0('Jbetatrendrem0 ~ ', paste(gsub('temptrend_abs.sc\\*', '', terms), collapse = ' + '))),
                             random = list(STUDY_ID = ~ 1, rarefyID = ~1), weights = varef, data = trends[i,], method = 'ML')
  
  for(j in 1:length(terms)){
    print(j)
    tryCatch({
      modTJbetadrops[[j+2]] <- lme(formula(paste0('Jbetatrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                   random = randef, weights = varef, data = trends[i,], method = 'ML')
    }, error = function(e){
      print('going to optim (Jbeta)')
      tryCatch({
        modTJbetadrops[[j+2]] <- lme(formula(paste0('Jbetatrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                     random = randef, weights = varef, data = trends[i,], method = 'ML',
                                     control = lmeControl(opt = 'optim'))
        
      }, error = function(e){
        print('going to more iters (Jbeta)') 
        tryCatch({
          modTJbetadrops[[j+2]] <- lme(formula(paste0('Jbetatrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                       random = randef, weights = varef, data = trends[i,], method = 'ML',
                                       control = lmeControl(maxIter = 100, msMaxIter = 100, 
                                                            niterEM = 50, msMaxEval = 500))
        }, error= function(e){
          print('giving up on this one (Jbeta)')
          modTJbetadrops[[j+2]] <- NA
        })
      }
      )
    }
    )
  }
  aicsJbeta <- sapply(modTJbetadrops, AICnas)
}

if(runHorn){
  i2 <- trends[, complete.cases(Horntrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                                temptrend_abs.sc, mass.sc, speed.sc, 
                                consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                                veg.sc, duration.sc, human_bowler.sc)]
  
  modTHorndrops <- vector('list', length(terms)+2)
  names(modTHorndrops) <- c('full', '-temptrend_abs.sc', paste0('-', terms))
  modTHorndrops[[1]] <- lme(formula(paste0('Horntrendrem0 ~ ', paste(terms, collapse = ' + '))),
                            random = randef, weights = varef, data = trends[i2,], method = 'ML')
  modTHorndrops[[2]] <- lme(formula(paste0('Horntrendrem0 ~ ', paste(gsub('temptrend_abs.sc\\*', '', terms), collapse = ' + '))),
                            random = list(STUDY_ID = ~ 1, rarefyID = ~1), weights = varef, data = trends[i2,], method = 'ML')
  
  for(j in 1:length(terms)){
    print(j)
    tryCatch({
      modTHorndrops[[j+2]] <- lme(formula(paste0('Horntrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                  random = randef, weights = varef, data = trends[i2,], method = 'ML')
    }, error = function(e){
      print('going to optim (Horn)')
      tryCatch({
        modTHorndrops[[j+2]] <- lme(formula(paste0('Horntrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                    random = randef, weights = varef, data = trends[i2,], method = 'ML',
                                    control = lmeControl(opt = 'optim'))
        
      }, error = function(e){
        print('going to more iters (Horn)') 
        tryCatch({
          modTHorndrops[[j+2]] <- lme(formula(paste0('Horntrendrem0 ~ ', paste(terms[-j], collapse = ' + '))),
                                      random = randef, weights = varef, data = trends[i2,], method = 'ML',
                                      control = lmeControl(maxIter = 100, msMaxIter = 100, 
                                                           niterEM = 50, msMaxEval = 500))
          
        }, error= function(e){
          print('giving up on this one (Horn)')
          modTHorndrops[[j+2]] <- NA
        })
      })
    })
  }
  aicsHorn <- sapply(modTHorndrops, AICnas)
}

# if there was anything new
if(runJtu | runJbeta | runHorn){
  if(!exists('aicsfromfull')){
    aicsfromfull <- data.frame(mod = names(aicsJtu))
  }
  
  # subtract full from each model AIC. Negative means term removal is supported. Positive means full is the better model.
  if(runJtu){
    aicsfromfull$dAIC_Jtu <- aicsJtu - aicsJtu[1]
  }
  if(runJbeta){
    aicsfromfull$dAIC_Jbeta <- aicsJbeta - aicsJbeta[1]
  }
  if(runHorn){
    aicsfromfull$dAIC_Horn <- aicsHorn - aicsHorn[1]
  }
  
  # write out
  write.csv(aicsfromfull, file = 'output/aics_from_full.csv', row.names = FALSE)
}

aicsfromfull
```

#### Plot deltaAICs for all 3 models
```{r plot dAICs}
# transform for a plot
aicsfromfulllong <- reshape(aicsfromfull, direction = 'long',
                            varying = c('dAIC_Jtu', 'dAIC_Jbeta', 'dAIC_Horn'),
                            v.names = 'dAIC',
                            idvar = 'mod',
                            timevar = 'type',
                            times = c('Jtu', 'Jbeta', 'Horn'))

trans = function(x) sign(x)*sqrt(abs(x))
aicsfromfulllong$dAIC_tr <- trans(aicsfromfulllong$dAIC)

# plot
xlims <- range(aicsfromfulllong$dAIC_tr, na.rm = TRUE)
xticks <- c(-10, 0, 10, 100, 1000, 10000)
par(mai = c(0.5, 3, 0.1, 0.1))
with(aicsfromfulllong[aicsfromfulllong$type == 'Jtu',], plot(dAIC_tr, nrow(aicsfromfull):1, 
                                                           col = 'light grey', xlim = xlims, yaxt = 'n', ylab = '', xaxt = 'n'))
with(aicsfromfulllong[aicsfromfulllong$type == 'Jbeta',], points(dAIC_tr, nrow(aicsfromfull):1 - 0.1, col = 'dark grey'))
with(aicsfromfulllong[aicsfromfulllong$type == 'Horn',], points(dAIC_tr, nrow(aicsfromfull):1 - 0.2, col = 'black'))
axis(2, at = nrow(aicsfromfull):1, labels = aicsfromfull$mod, las = 1, cex.axis = 0.7)
axis(1, at = trans(xticks), labels = xticks, cex.axis = 0.5)
abline(v = 0, lty =2, col = 'grey')
```

Light grey is for Jaccard turnover, dark grey is for Jaccard total, black is for Morisita-Horn.
Clear that removing temperature trend makes the model quite a bit worse and has the biggest effect.



## Simplify the full models
This takes a couple days on a laptop to run.
```{r simplify the full models}
i1 <- trends[, complete.cases(Jtutrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i2 <- trends[, complete.cases(Jbetatrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i3 <- trends[, complete.cases(Horntrendrem0, REALM, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

# simplify the full models
if(file.exists('temp/modTsimpJturem0.rds')){
  modTsimpJturem0 <- readRDS('temp/modTsimpJturem0.rds')
} else {
  modTfullJturem0ML <- lme(Jtutrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i1,], method = 'ML',
                       control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))
  modTsimpJturem0 <- stepAIC(modTfullJturem0ML, direction = 'backward')
  saveRDS(modTsimpJturem0, file = 'temp/modTsimpJturem0.rds')
}

if(file.exists('temp/modTsimpJbetarem0.rds')){
  modTsimpJbetarem0 <- readRDS('temp/modTsimpJbetarem0.rds')
} else {
  modTfullJbetarem0ML <- lme(Jbetatrendrem0 ~ temptrend_abs.sc*REALM + 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc:REALM2,
                       random = randef, weights = varef, data = trends[i2,], method = 'ML')
  modTsimpJbetarem0 <- stepAIC(modTfullJbetarem0ML, direction = 'backward')
  saveRDS(modTsimpJbetarem0, file = 'temp/modTsimpJbetarem0.rds')
}

if(file.exists('temp/modTsimpHornrem0.rds')){
  modTsimpHornrem0 <- readRDS('temp/modTsimpHornrem0.rds')
} else {
  modTfullHornrem0ML <- lme(Horntrendrem0 ~ temptrend_abs.sc*REALM + 
                        temptrend_abs.sc*tsign +
                        temptrend_abs.sc*tempave_metab.sc + 
                        temptrend_abs.sc*seas.sc + 
                        temptrend_abs.sc*microclim.sc + 
                        temptrend_abs.sc*mass.sc + 
                        temptrend_abs.sc*speed.sc + 
                        temptrend_abs.sc*consumerfrac.sc +
                        temptrend_abs.sc*nspp.sc +
                        temptrend_abs.sc*thermal_bias.sc:tsign +
                        temptrend_abs.sc*npp.sc +
                        temptrend_abs.sc*veg.sc +
                        temptrend_abs.sc*duration.sc +
                        temptrend_abs.sc*human_bowler.sc:REALM2,
                      random = randef, weights = varef, data = trends[i3,], method = 'ML')
  modTsimpHornrem0 <- stepAIC(modTfullHornrem0ML, direction = 'backward')
  saveRDS(modTsimpHornrem0, file = 'temp/modTsimpHornrem0.rds')
}

summary(modTsimpJturem0)
summary(modTsimpJbetarem0)
summary(modTsimpHornrem0)


```


## Make realm-specific models
```{r LME Horn models by realm, fig.width=10, fig.height=8}
i1 <- trends[, REALM == 'Terrestrial' & complete.cases(Horntrendrem0, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)]
i2 <- trends[, REALM == 'Freshwater' & complete.cases(Horntrendrem0, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             nspp.sc, thermal_bias.sc, npp.sc, 
                             veg.sc, duration.sc, human_bowler.sc)] # no consumerfrac
i3 <- trends[, REALM == 'Marine' & complete.cases(Horntrendrem0, tempave_metab.sc, seas.sc, microclim.sc, 
                             temptrend_abs.sc, mass.sc, speed.sc, 
                             consumerfrac.sc, nspp.sc, thermal_bias.sc, npp.sc, 
                             duration.sc, human_bowler.sc)] # no veg

print(paste('Terrestrial', sum(i1)))
print(paste('Freshwater', sum(i2)))
print(paste('Marine', sum(i3)))

randef <- list(STUDY_ID = ~ temptrend_abs.sc, rarefyID = ~1)
varef <- varPower(-0.5, ~nyrBT)

# land
if(file.exists('temp/modTfullHornTerr.rds')){
  modTfullHornTerr <- readRDS('temp/modTfullHornTerr.rds')
} else {
  modTfullHornTerr <- lme(Horntrendrem0 ~ 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc,
                       random = randef, weights = varef, data = trends[i1,], method = 'REML',
                       control = lmeControl(maxIter = 100, msMaxIter = 100, niterEM = 50, msMaxEval = 500))
  saveRDS(modTfullHornTerr, file = 'temp/modTfullHornTerr.rds')
}

# freshwater
if(file.exists('temp/modTfullHornFresh.rds')){
  modTfullHornFresh <- readRDS('temp/modTfullHornFresh.rds')
} else {
  modTfullHornFresh <- lme(Horntrendrem0 ~ 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*veg.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc,
                       random = randef, weights = varef, data = trends[i2,], method = 'REML')
  saveRDS(modTfullHornFresh, file = 'temp/modTfullHornFresh.rds')
}

# marine
if(file.exists('temp/modTfullHornMar.rds')){
  modTfullHornMar <- readRDS('temp/modTfullHornMar.rds')
} else {
  modTfullHornMar <- lme(Horntrendrem0 ~ 
                         temptrend_abs.sc*tsign +
                         temptrend_abs.sc*tempave_metab.sc + 
                         temptrend_abs.sc*seas.sc + 
                         temptrend_abs.sc*microclim.sc + 
                         temptrend_abs.sc*mass.sc + 
                         temptrend_abs.sc*speed.sc + 
                         temptrend_abs.sc*consumerfrac.sc +
                         temptrend_abs.sc*nspp.sc +
                         temptrend_abs.sc*thermal_bias.sc:tsign +
                         temptrend_abs.sc*npp.sc +
                         temptrend_abs.sc*duration.sc +
                         temptrend_abs.sc*human_bowler.sc,
                       random = randef, weights = varef, data = trends[i3,], method = 'REML')
  saveRDS(modTfullHornMar, file = 'temp/modTfullHornMar.rds')
}

summary(modTfullHornTerr)
summary(modTfullHornFresh)
summary(modTfullHornMar)
```
### Plot the realm-specific coefficients
Also uses the full models across all realms
```{r plot realm mods, fig.height=12, fig.width=9}

coefs1 <- summary(modTfullHornrem0)$tTable
coefs2 <- summary(modTfullHornTerr)$tTable
coefs3 <- summary(modTfullHornFresh)$tTable
coefs4 <- summary(modTfullHornMar)$tTable

varstoplot <- unique(c(rownames(coefs1), rownames(coefs2), rownames(coefs3), rownames(coefs4)))
varstoplot <- varstoplot[which(!grepl('Intercept', varstoplot) | grepl(':', varstoplot))] # vars to plot

rows1_1 <- which(rownames(coefs1) %in% varstoplot) # rows in coefs
rows1_2 <- which(rownames(coefs2) %in% varstoplot)
rows1_3 <- which(rownames(coefs3) %in% varstoplot)
rows1_4 <- which(rownames(coefs4) %in% varstoplot)
xlims <- range(c(coefs1[rows1_1,1] - coefs1[rows1_1,2], coefs1[rows1_1,1] + coefs1[rows1_1,2], 
                 coefs2[rows1_2,1] - coefs2[rows1_2,2], coefs2[rows1_2,1] + coefs2[rows1_2,2], 
                 coefs3[rows1_3,1] - coefs3[rows1_3,2], coefs3[rows1_3,1] + coefs3[rows1_3,2],
                 coefs4[rows1_4,1] - coefs4[rows1_4,2], coefs4[rows1_4,1] + coefs4[rows1_4,2]))


cols <- brewer.pal(4, 'Dark2') # for full, terr, fresh, mar
pchs <- c(1, 16, 16, 16)
offs <- c(0.1, 0, -0.1, -0.2) # offset vertically for each model


par(las = 1, mai = c(0.5, 4, 0.1, 0.1))

plot(0,0, col = 'white', xlim = xlims, ylim = c(1,length(varstoplot)), yaxt='n', xlab = '', ylab ='')
axis(2, at = length(varstoplot):1, labels = varstoplot, cex.axis = 0.7)
abline(v = 0, col = 'grey', lty = 2)
abline(h = 1:length(varstoplot), col = 'grey', lty = 3)
for(i in 1:length(varstoplot)){
  if(varstoplot[i] %in% rownames(coefs1)){
    x = coefs1[rownames(coefs1) == varstoplot[i], 1]
    se = coefs1[rownames(coefs1) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[1], pch = pchs[1], col = cols[1])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[1], length(varstoplot) + 1 - i + offs[1]), col = cols[1])
  }
  if(varstoplot[i] %in% rownames(coefs2)){
    x = coefs2[rownames(coefs2) == varstoplot[i], 1]
    se = coefs2[rownames(coefs2) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[2], pch = pchs[2], col = cols[2])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[2], length(varstoplot) + 1 - i + offs[2]), col = cols[2])
  }
  if(varstoplot[i] %in% rownames(coefs3)){
    x = coefs3[rownames(coefs3) == varstoplot[i], 1]
    se = coefs3[rownames(coefs3) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[3], pch = pchs[3], col = cols[3])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[3], length(varstoplot) + 1 - i + offs[3]), col = cols[3])
  }
  if(varstoplot[i] %in% rownames(coefs4)){
    x = coefs4[rownames(coefs4) == varstoplot[i], 1]
    se = coefs4[rownames(coefs4) == varstoplot[i], 2]
    points(x, length(varstoplot) + 1 - i + offs[4], pch = pchs[4], col = cols[4])
    lines(x = c(x-se, x+se), y = c(length(varstoplot) + 1 - i + offs[4], length(varstoplot) + 1 - i + offs[4]), col = cols[4])
  }
}
legend('bottomleft', col = cols, pch = pchs, lwd = 1, legend = c('All', 'Terestrial', 'Freshwater', 'Marine'))
```

[End text in hopes this helps the last figure show up when knitted]